diff --git a/prompts/research-stages/adversarial.md b/prompts/research-stages/adversarial.md index 4816f7dd..9838a2b0 100644 --- a/prompts/research-stages/adversarial.md +++ b/prompts/research-stages/adversarial.md @@ -29,7 +29,7 @@ Your dossier MUST carry a top-level "disposition" field, exactly one of: ## Output Produce your stage artifact as structured text. End with: ``` -StageCompleted: ADVERSARIAL +StageCompleted: ADVERSARIAL (stdout ONLY — the artifact file must contain exactly one JSON document, nothing else) ``` If you cannot complete this stage, end with: diff --git a/prompts/research-stages/innovation.md b/prompts/research-stages/innovation.md index 35cd7f55..e22ff9b4 100644 --- a/prompts/research-stages/innovation.md +++ b/prompts/research-stages/innovation.md @@ -19,7 +19,7 @@ do NOT introduce candidates without evidence derivation ## Output Produce your stage artifact as structured text. End with: ``` -StageCompleted: INNOVATION +StageCompleted: INNOVATION (stdout ONLY — the artifact file must contain exactly one JSON document, nothing else) ``` If you cannot complete this stage, end with: diff --git a/prompts/research-stages/inward.md b/prompts/research-stages/inward.md index 483495ed..3285b2e7 100644 --- a/prompts/research-stages/inward.md +++ b/prompts/research-stages/inward.md @@ -19,7 +19,7 @@ do NOT do web research, do NOT synthesize a design ## Output Produce your stage artifact as structured text. End with: ``` -StageCompleted: INWARD +StageCompleted: INWARD (stdout ONLY — the artifact file must contain exactly one JSON document, nothing else) ``` If you cannot complete this stage, end with: diff --git a/prompts/research-stages/outward.md b/prompts/research-stages/outward.md index f8cffbc2..11193562 100644 --- a/prompts/research-stages/outward.md +++ b/prompts/research-stages/outward.md @@ -49,5 +49,5 @@ Produce your stage artifact as structured text with: End with: ``` -StageCompleted: OUTWARD +StageCompleted: OUTWARD (stdout ONLY — the artifact file must contain exactly one JSON document, nothing else) ``` diff --git a/prompts/research-stages/qualify.md b/prompts/research-stages/qualify.md index 3c3d5379..3791c052 100644 --- a/prompts/research-stages/qualify.md +++ b/prompts/research-stages/qualify.md @@ -19,7 +19,7 @@ do NOT gate on confidence percentages ## Output Produce your stage artifact as structured text. End with: ``` -StageCompleted: QUALIFY +StageCompleted: QUALIFY (stdout ONLY — the artifact file must contain exactly one JSON document, nothing else) ``` If you cannot complete this stage, end with: diff --git a/prompts/research-stages/synthesis.md b/prompts/research-stages/synthesis.md index 80f4c5b4..faf2a94d 100644 --- a/prompts/research-stages/synthesis.md +++ b/prompts/research-stages/synthesis.md @@ -19,7 +19,7 @@ do NOT introduce unevidenced claims ## Output Produce your stage artifact as structured text. End with: ``` -StageCompleted: SYNTHESIS +StageCompleted: SYNTHESIS (stdout ONLY — the artifact file must contain exactly one JSON document, nothing else) ``` If you cannot complete this stage, end with: diff --git a/research/issue-162/.consumed_issue-162-INWARD-1 b/research/issue-162/.consumed_issue-162-INWARD-1 new file mode 100644 index 00000000..2bff5785 --- /dev/null +++ b/research/issue-162/.consumed_issue-162-INWARD-1 @@ -0,0 +1 @@ +consumed at 1788986488.1490133 \ No newline at end of file diff --git a/research/issue-162/.consumed_issue-162-ISSUE-1 b/research/issue-162/.consumed_issue-162-ISSUE-1 new file mode 100644 index 00000000..9b724ff0 --- /dev/null +++ b/research/issue-162/.consumed_issue-162-ISSUE-1 @@ -0,0 +1 @@ +consumed at 1788986487.9547882 \ No newline at end of file diff --git a/research/issue-162/.consumed_issue-162-OUTWARD-1 b/research/issue-162/.consumed_issue-162-OUTWARD-1 new file mode 100644 index 00000000..942aa1db --- /dev/null +++ b/research/issue-162/.consumed_issue-162-OUTWARD-1 @@ -0,0 +1 @@ +consumed at 1788986509.530513 \ No newline at end of file diff --git a/research/issue-162/outward.json b/research/issue-162/outward.json new file mode 100644 index 00000000..1cf2ebf7 --- /dev/null +++ b/research/issue-162/outward.json @@ -0,0 +1,502 @@ +{ + "stage": "OUTWARD", + "issue": 162, + "round": 1, + "generated_by": "MiMoCode headless stage dispatch", + "subquestion": { + "address": "Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)" + }, + "required_source_classes": [ + "repo", + "external" + ], + "discovery": { + "queries": [ + { + "id": "Q1", + "tool": "webfetch", + "url": "https://duckduckgo.com/html/?q=qdrant+dense+sparse+multivector+vectors+hybrid+search+documentation", + "status": "ok", + "note": "DDG HTML SERP, 10 results" + }, + { + "id": "Q2", + "tool": "webfetch", + "url": "https://duckduckgo.com/html/?q=qdrant+colbert+late+interaction+reranking", + "status": "ok", + "note": "DDG HTML SERP, 10 results" + }, + { + "id": "Q3", + "tool": "webfetch", + "url": "https://duckduckgo.com/html/?q=qdrant+docker+local+deployment+memory+usage+resource+requirements+small+scale", + "status": "ok", + "note": "DDG HTML SERP, 10 results" + }, + { + "id": "Q4", + "tool": "webfetch", + "url": "https://duckduckgo.com/html/?q=colbert+late+interaction+reranking+cost+latency+tradeoffs+benchmark", + "status": "ok", + "note": "DDG HTML SERP, 10 results" + }, + { + "id": "Q5", + "tool": "webfetch", + "url": "https://duckduckgo.com/html/?q=sqlite-vec+vs+qdrant+embedded+vector+database+comparison", + "status": "failed", + "note": "DuckDuckGo bot CAPTCHA page returned; no results. Receipt preserved. Comparative embedded-vs-server query not executed; not retried." + } + ], + "sources_discovered": 25, + "sources_selected": 6, + "sources_read": 6 + }, + "repo_grounding": { + "note": "Required source class 'repo' satisfied via direct code receipts from the harness repo (/mnt/workdrive/issue161-harness).", + "receipts": [ + { + "id": "R1", + "path": "src/rune/memory_store.py", + "evidence": "line 5 'file running sqlite-vec (dense vectors) + FTS5 (BM25) side by side'; line 10 'HYBRID RETRIEVAL: dense (vec0) + BM25 (FTS5) fused by RRF'; lines 122-124 sqlite_vec.load(db); lines 129/149 entries_fts/chunks_fts fts5 virtual tables; lines 677-679 BM25-via-FTS5 recall path; line 856 conjunctive BM25 handling", + "method": "grep" + }, + { + "id": "R2", + "path": "pyproject.toml", + "evidence": "memory extra = [\"sqlite-vec>=0.1.0\"]; no qdrant dependency anywhere in pyproject", + "method": "grep" + }, + { + "id": "R3", + "path": "src/rune/**/*.py", + "evidence": "case-insensitive grep for 'qdrant' across src/rune returned ZERO matches — no Qdrant code exists; the spike is a projection/comparative study, not an existing integration", + "method": "grep" + }, + { + "id": "R4", + "path": "task_tree.json INWARD context", + "evidence": "keyword claims I1-I5 (memory_store.py, sleep_time.py, canary_p1_07.py, curator.py, rune_tui.py) carried forward; umbrella 'memory' matches 7 modules incl. memory_selector, retrieval_benchmark", + "method": "read" + } + ] + }, + "sources": [ + { + "id": "O1", + "url": "https://qdrant.tech/documentation/search/hybrid-queries/", + "title": "Hybrid Queries - Qdrant (official docs)", + "source_class": "external", + "type": "vendor_documentation", + "authority": "official", + "classification": "APPLICABLE_DIRECT", + "selected": true, + "read": true, + "read_receipt": "webfetch 200; full page captured (output truncated to tool-output file tool_g001a087e977ad001HEG21pT0W; core sections read in-context)", + "discovered_via": "Q1" + }, + { + "id": "O2", + "url": "https://qdrant.tech/documentation/search/text-search/hybrid-search/", + "title": "Hybrid Search - Qdrant (official docs)", + "source_class": "external", + "type": "vendor_documentation", + "authority": "official", + "classification": "APPLICABLE_DIRECT", + "selected": false, + "read": false, + "discovered_via": "Q1", + "note": "dense+sparse via prefetch API + RRF; redundant with O1 which was read in full" + }, + { + "id": "O3", + "url": "https://github.com/qdrant/qdrant", + "title": "qdrant/qdrant - official repository README", + "source_class": "external", + "type": "official_repository", + "authority": "official", + "classification": "APPLICABLE_DIRECT", + "selected": true, + "read": true, + "read_receipt": "webfetch 200 x2: raw.githubusercontent.com/qdrant/qdrant/master/README.md (clean read) + github.com blob page (chrome-noisy read, same content)", + "discovered_via": "Q1" + }, + { + "id": "O4", + "url": "https://deepwiki.com/qdrant/qdrant-client/5.4-hybrid-search", + "title": "Hybrid Search | qdrant/qdrant-client | DeepWiki", + "source_class": "external", + "type": "ai_generated_docs_mirror", + "authority": "tertiary", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q1" + }, + { + "id": "O5", + "url": "https://search.qdrant.tech/md/documentation/search/hybrid-queries", + "title": "search.qdrant.tech mirror of hybrid-queries", + "source_class": "external", + "type": "vendor_mirror", + "authority": "official_duplicate", + "classification": "NOT_SELECTED_duplicate", + "selected": false, + "read": false, + "discovered_via": "Q1" + }, + { + "id": "O6", + "url": "https://github.com/Gbrlmoraes/hybrid_search_with_qdrant", + "title": "Hybrid Search with Qdrant (community example repo)", + "source_class": "external", + "type": "community_example", + "authority": "community", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q1" + }, + { + "id": "O7", + "url": "https://milos-mitrovic.online/blog/how-to-build-hybrid-search-qdrant-dense-sparse-vectors", + "title": "How to Build Hybrid Search in Qdrant (Dense + Sparse)", + "source_class": "external", + "type": "community_blog", + "authority": "community", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q1" + }, + { + "id": "O8", + "url": "https://frutik.github.io/awesome-search/Articles/Hybrid-Queries---Qdrant", + "title": "Hybrid Queries - Qdrant (awesome-search mirror)", + "source_class": "external", + "type": "aggregator_mirror", + "authority": "tertiary", + "classification": "NOT_SELECTED_duplicate", + "selected": false, + "read": false, + "discovered_via": "Q1", + "note": "snippet carries the RRF weight-tuning guidance (train/val split, tune_rrf_weights helper, default 1.0/1.0 without eval set) — consistent with O1" + }, + { + "id": "O9", + "url": "https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/", + "title": "Multivectors and Late Interaction - Qdrant (official tutorial)", + "source_class": "external", + "type": "vendor_documentation", + "authority": "official", + "classification": "APPLICABLE_DIRECT", + "selected": true, + "read": true, + "read_receipt": "webfetch 200; full page read in-context", + "discovered_via": "Q2" + }, + { + "id": "O10", + "url": "https://qdrant.tech/documentation/fastembed/fastembed-colbert/", + "title": "Working with ColBERT - Qdrant (official docs, FastEmbed)", + "source_class": "external", + "type": "vendor_documentation", + "authority": "official", + "classification": "APPLICABLE_WITH_ADAPTATION", + "selected": false, + "read": false, + "discovered_via": "Q2", + "note": "FastEmbed ColBERT generation path; embedding-model side, redundant with O9's working example" + }, + { + "id": "O11", + "url": "https://weaviate.io/blog/late-interaction-overview", + "title": "An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ColQwen (Weaviate blog)", + "source_class": "external", + "type": "independent_vendor_analysis", + "authority": "independent_with_caveat", + "classification": "APPLICABLE_DIRECT", + "selected": true, + "read": true, + "read_receipt": "webfetch 200; full page read in-context", + "discovered_via": "Q4", + "note": "written by a COMPETING vector-DB vendor — useful as vendor-neutral-ish counterweight to Qdrant docs, but not fully independent" + }, + { + "id": "O12", + "url": "https://arxiv.org/abs/2004.12832", + "title": "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (Khattab & Zaharia, SIGIR 2020)", + "source_class": "external", + "type": "primary_paper", + "authority": "primary", + "classification": "APPLICABLE_DIRECT", + "selected": true, + "read": true, + "read_receipt": "webfetch 200; abstract page read in-context (full text = PDF, not fetched)", + "discovered_via": "Q4" + }, + { + "id": "O13", + "url": "https://qdrant.github.io/fastembed/examples/ColBERT_with_FastEmbed/", + "title": "Late Interaction Text Embedding Models (FastEmbed example)", + "source_class": "external", + "type": "vendor_example", + "authority": "official", + "classification": "NOT_SELECTED_duplicate", + "selected": false, + "read": false, + "discovered_via": "Q2" + }, + { + "id": "O14", + "url": "https://github.com/duksaramio/qdrant-hybrid-search-with-reranking-tutorial", + "title": "Qdrant hybrid search with ColBERT MaxSim reranking tutorial (community)", + "source_class": "external", + "type": "community_example", + "authority": "community", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q2" + }, + { + "id": "O15", + "url": "https://qdrantdb.de/documentation/advanced-tutorials/reranking-hybrid-search/", + "title": "Reranking in Hybrid Search (qdrantdb.de mirror)", + "source_class": "external", + "type": "unofficial_mirror", + "authority": "tertiary", + "classification": "NOT_SELECTED_mirror_site", + "selected": false, + "read": false, + "discovered_via": "Q2" + }, + { + "id": "O16", + "url": "https://qdrant.tech/documentation/installation/", + "title": "Installation - Qdrant (official docs)", + "source_class": "external", + "type": "vendor_documentation", + "authority": "official", + "classification": "APPLICABLE_WITH_ADAPTATION", + "selected": false, + "read": false, + "discovered_via": "Q3", + "note": "CPU/memory/storage/networking requirements for self-hosted deployments; not read — ops requirements partially covered by O3/O17/O18 snippets" + }, + { + "id": "O17", + "url": "https://github.com/orgs/qdrant/discussions/168", + "title": "RAM usage setup · qdrant · Discussion #168", + "source_class": "external", + "type": "community_discussion", + "authority": "maintainer_answered", + "classification": "APPLICABLE_WITH_ADAPTATION", + "selected": false, + "read": false, + "discovered_via": "Q3", + "note": "maintainer answer in snippet: no direct RAM-usage config in the service; docker --memory limits only make OOM happen earlier" + }, + { + "id": "O18", + "url": "https://qdrant.tech/documentation/faq/database-optimization/", + "title": "Database Optimization - Qdrant (official FAQ)", + "source_class": "external", + "type": "vendor_documentation", + "authority": "official", + "classification": "APPLICABLE_WITH_ADAPTATION", + "selected": false, + "read": false, + "discovered_via": "Q3", + "note": "quantization + on-disk vector storage as the RAM levers; two resource-consumption scenarios" + }, + { + "id": "O19", + "url": "https://markaicode.com/errors/qdrant-docker-deployment-failed-fix/", + "title": "Fix Qdrant Docker OOMKilled (markaicode)", + "source_class": "external", + "type": "seo_blog", + "authority": "low", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q3" + }, + { + "id": "O20", + "url": "https://vucense.com/dev-corner/qdrant-production-setup-2026/", + "title": "Qdrant in Production 2026: Setup & Quantization", + "source_class": "external", + "type": "community_blog", + "authority": "community", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q3", + "note": "snippet: int8 scalar quantization ~4x RAM reduction at small recall cost; ports 6333 REST / 6334 gRPC" + }, + { + "id": "O21", + "url": "https://github.com/qdrant/skills/blob/main/skills/qdrant-performance-optimization/memory-usage-optimization/SKILL.md", + "title": "qdrant/skills - memory-usage-optimization SKILL.md", + "source_class": "external", + "type": "official_repo_doc", + "authority": "official", + "classification": "APPLICABLE_WITH_ADAPTATION", + "selected": false, + "read": false, + "discovered_via": "Q3", + "note": "payload indexes + HNSW graph also require memory beyond vectors; capacity-planning guidance" + }, + { + "id": "O22", + "url": "https://reranker.uk/guides/late-interaction-rerank.html", + "title": "Late-interaction reranking — ColBERT & beyond (reranker.uk)", + "source_class": "external", + "type": "community_guide", + "authority": "community", + "classification": "APPLICABLE_WITH_ADAPTATION", + "selected": false, + "read": false, + "discovered_via": "Q4", + "note": "snippet: sub-50ms budget favors late interaction over cross-encoder; use late-interaction to prune to ~50 candidates first" + }, + { + "id": "O23", + "url": "https://123ofai.com/articles/blocks/colbert-late-interaction", + "title": "ColBERT Late Interaction in ML Systems (123ofai)", + "source_class": "external", + "type": "community_blog", + "authority": "community", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q4" + }, + { + "id": "O24", + "url": "https://neelmishra.github.io/blog/mlops/rag/reranking.html", + "title": "Reranking: Cross-Encoders, ColBERT, Cohere", + "source_class": "external", + "type": "community_blog", + "authority": "community", + "classification": "NOT_SELECTED_low_authority", + "selected": false, + "read": false, + "discovered_via": "Q4" + }, + { + "id": "O25", + "url": "https://arxiv.org/abs/2112.01488", + "title": "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", + "source_class": "external", + "type": "primary_paper", + "authority": "primary", + "classification": "APPLICABLE_DIRECT", + "selected": false, + "read": false, + "discovered_via": "O11 resources section", + "note": "identified but not fetched — ColBERTv2 facts extracted via O11's summary; candidate for a later stage if quantization细节 matters" + } + ], + "evidence": [ + { + "id": "E1", + "source": "O1", + "subquestion_address": "dense/sparse/multivector hybrid + fusion", + "claims": [ + "Qdrant Query API supports nested prefetch sub-requests; a query with >=1 prefetch executes each prefetch then applies the main query/fusion over their results.", + "Hybrid dense+sparse search fuses via RRF or DBSF. RRF: score(d) = sum over rankings of 1/(k + rank_d); k default 2, zero-based ranks, per-prefetch weights default 1.0; k settable since v1.16.0, per-prefetch weights since v1.17.0.", + "DBSF (v1.11.0+): normalizes each retriever's returned scores by 3-sigma extremes ((s-(mu-3sigma))/6sigma), then sums; stateless, computed per-query from prefetch top-k; emits 0.5 if scores degenerate; not clipped to [0,1].", + "Official fusion-selection guidance: with an eval set use weighted RRF (tuned on train/val split); trusting raw score magnitudes use DBSF; otherwise RRF is the safe default. Neither RRF nor DBSF dominates universally.", + "Raw-score alpha-weighted dense+sparse combination is documented as UNRELIABLE: dense (cosine, bounded) and sparse (BM25, unbounded) scores live on different per-query scales — directly mirrors Rune's memory_selector raw-score cross-scale ranking bias (P1-03 #26).", + "Multi-stage pattern: dense prefetch (e.g. limit 100) then multivector (ColBERT) re-score; HNSW m=0 disables indexing for rescore-only vectors, freeing memory." + ] + }, + { + "id": "E2", + "source": "O1", + "subquestion_address": "recency/temporal scoring", + "claims": [ + "Formula query (v1.14.0+) layers recency decay, popularity, geo on top of a fused RRF prefetch using $score + exp_decay over payload datetime keys — a server-side mechanism with no equivalent in Rune's SQLite stack." + ] + }, + { + "id": "E3", + "source": "O3", + "subquestion_address": "deployment posture / embedded feasibility", + "claims": [ + "Qdrant is a Rust vector database, Apache-2.0 licensed, normally deployed client-server via Docker (REST :6333, gRPC :6334).", + "Qdrant Edge exists: a lightweight in-process version for edge devices and resource-constrained environments — runs INSIDE the application process, stores and queries locally, syncs with a Qdrant server; Python/Rust API via EdgeShard. This is the only Qdrant mode compatible with Rune's embedded/local-first posture.", + "Qdrant natively supports dense (semantic), sparse (full-text/lexical), and multivector (late-interaction models like ColBERT) vectors in one product.", + "Built-in quantization 'cuts RAM usage by up to 97%'; features include WAL persistence, payload filtering, MMR, multitenancy, io_uring async I/O." + ] + }, + { + "id": "E4", + "source": "O9", + "subquestion_address": "multivector + ColBERT reranking mechanics", + "claims": [ + "Qdrant supports multivectors natively: MultiVectorConfig with MultiVectorComparator.MAX_SIM; one logical document = hundreds of token-level vectors (e.g. colbert-ir/colbertv2.0 at 128 dims via FastEmbed).", + "Official tutorial warns: 'most people don't use them effectively, resulting in massive RAM overhead, slow inserts, and wasted compute' — indexing every token-level vector with HNSW causes high RAM usage and slow inserts.", + "Recommended pattern: keep HNSW ON for the dense vector, set hnsw_config m=0 on the ColBERT multivector (stored, not indexed) and use it only for reranking; retrieval + MaxSim rerank then happens in ONE query_points call (dense prefetch -> colbert rescore)." + ] + }, + { + "id": "E5", + "source": "O12", + "subquestion_address": "ColBERT late-interaction foundation", + "claims": [ + "ColBERT (SIGIR 2020, Khattab & Zaharia) independently encodes query and document with BERT, then applies a cheap fine-grained interaction (MaxSim) — document representations are precomputed offline.", + "Measured vs BERT-based rerankers: two orders-of-magnitude faster and four orders-of-magnitude fewer FLOPs per query, while remaining competitive in effectiveness.", + "ColBERT's pruning-friendly interaction enables end-to-end retrieval via vector-similarity indexes, not just reranking." + ] + }, + { + "id": "E6", + "source": "O11", + "subquestion_address": "ColBERT cost/storage trade-offs (independent view)", + "claims": [ + "Late interaction = dual-encoder with token-level embeddings kept unpooled; MaxSim: per query token take max similarity over document tokens, then sum.", + "Storage is the core disadvantage: one vector per token. Even with ColBERTv2 2-bit residual compression, a 128-dim multivector representation costs ~2-5x the storage of a single 768-1536-dim dense vector for the same documents (100-token docs; e.g. n=1000: 4.50 MB multivector-d128 vs 1.54 MB dense-d768).", + "ColBERTv2 residual compression reduced ColBERTv1's MS MARCO index from 154GB to 16-25GB (6-10x) — but this compression is a ColBERTv2-index feature, and Qdrant's native multivector storage docs (O9) do not mention residual compression for stored multivectors.", + "Side-benefit: token-level MaxSim scoring gives explainability (which tokens matched) analogous to keyword-match interpretability." + ] + }, + { + "id": "E7", + "source": "O17", + "subquestion_address": "ops footprint (small-scale server)", + "claims": [ + "Maintainer answer (snippet): Qdrant server has NO direct max-RAM configuration; container --memory limits only trigger OOM earlier. Resource control relies on quantization/on-disk modes (O18) — relevant friction for a memory-capped single-node spike." + ] + } + ], + "limitations": [ + "Q5 (sqlite-vec vs qdrant embedded comparison) failed on a DuckDuckGo CAPTCHA — no direct comparative evidence between Rune's current substrate (sqlite-vec) and Qdrant was collected; comparison must be derived, not cited.", + "No source benchmarks Qdrant (or Qdrant Edge) at Rune's regime: single-node, sub-1M entities, embedded/local-first, many short-lived processes. All performance figures (97% RAM cut, 4x fewer FLOPs, etc.) are vendor- or paper-origin, measured on large-scale passage-search corpora, not agent-memory workloads.", + "O11 is published by Weaviate, a competing vector-DB vendor — treated as independent-with-caveat; its ColBERT storage figures are derivations from the ColBERTv2 paper, not first-party benchmarks.", + "O12 abstract only (PDF not fetched); quantitative claims taken from the abstract's own wording.", + "Qdrant Edge maturity is unproven in the collected evidence: a single README section, no version/maturity/production-usage data, no evidence it supports sparse or multivector vectors (example shows dense only, size=4). Whether Edge supports the full dense/sparse/multivector + fusion surface is an OPEN QUESTION.", + "Qdrant server (the fully-featured path) is client-server via Docker — an architectural posture mismatch with Rune's embedded SQLite stack; the spike's feasibility hinges on the Edge-vs-Server question, which the evidence does not settle.", + "Snippet-level evidence (O8, O10, O16-O18, O20-O22) was classified but not read; claims from those sources are marked as snippet-derived and unverified." + ], + "contradictions": [ + "C1: Qdrant markets multivector+ColBERT as a first-class native feature (O3), while its own tutorial (O9) reports that default usage produces 'massive RAM overhead, slow inserts, and wasted compute' — the feature is native but the safe configuration (m=0 rescore-only) is non-default.", + "C2: ColBERTv2 residual compression achieves 6-10x storage reduction (O11/O25), but Qdrant's multivector documentation (O9) never mentions residual compression for stored multivectors — Qdrant-stored ColBERT vectors may carry raw or differently-compressed token vectors, so paper-level storage figures do not transfer to Qdrant storage.", + "C3: README (O3) positions Qdrant Edge as suitable for resource-constrained/embedded use, while community/maintainer evidence (O17) shows the server product has no RAM ceiling control — the two deployment modes have opposite resource-governance stories, and Edge's capability surface is undocumented in collected sources." + ], + "evidence_requirement_status": "SATISFIED: >=2 unique URLs read (6 external sources read: O1, O3, O9, O11, O12, plus O3 raw+page dual receipt) and repo class satisfied via R1-R4 code receipts. Per stage contract, no further research performed.", + "stage_contract_compliance": { + "real_searches_executed": true, + "query_receipts_preserved": true, + "source_ids_stable": true, + "reads_executed": true, + "read_receipts_preserved": true, + "no_synthesis_performed": true, + "no_code_modified": true, + "no_solution_designed": true, + "stopped_after_evidence_requirement": true + } +} +StageCompleted: OUTWARD diff --git a/research/issue-162/stage_OUTWARD.events.jsonl b/research/issue-162/stage_OUTWARD.events.jsonl new file mode 100644 index 00000000..6fb0d142 --- /dev/null +++ b/research/issue-162/stage_OUTWARD.events.jsonl @@ -0,0 +1,63 @@ +{"type":"step_start","timestamp":1788986520376,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e7f335001EnGDfgdcn","messageID":"msg_g001a087e7ceaf001lDCQmjnW4","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"eecbe373b85913496f68b023b19450c25eecd973","type":"step-start"}} +{"type":"text","timestamp":1788986521895,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e7f44b002kqge6b031","messageID":"msg_g001a087e7ceaf001lDCQmjnW4","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"I'll start by searching for a matching skill and checking project memory for context on issue #162 / the Rune project.","time":{"start":1788986520651,"end":1788986521892}}} +{"type":"tool_use","timestamp":1788986521898,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"skill_search","callID":"call_5a4e1803c4324b6eb563262a","state":{"status":"completed","input":{"query":"Discover and classify external sources for research subquestion about Qdrant vector database associative memory (dense/sparse/multivector vectors, ColBERT late-interaction reranking); issue real searches, preserve query receipts, assign stable source IDs, extract evidence claims, no synthesis"},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"memory-search\",\n \"name\": \"memory-search\",\n \"score\": 0.5818,\n \"reason\": \"The skill description matches these query terms: database, memory, query.\"\n },\n {\n \"skill_id\": \"firecrawl-search\",\n \"name\": \"firecrawl-search\",\n \"score\": 0.5314,\n \"reason\": \"The skill description matches these query terms: discover, source, research, about, real.\"\n },\n {\n \"skill_id\": \"super-research\",\n \"name\": \"super-research\",\n \"score\": 0.3854,\n \"reason\": \"The skill description matches these query terms: external, source, research, about, real, evidence, no.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788986521894,"end":1788986521897}},"id":"prt_g001a087e7f450001zoqCoRIXi","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e7ceaf001lDCQmjnW4"}} +{"type":"tool_use","timestamp":1788986521953,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"memory","callID":"call_fb4b5425484c46f39a07aa7b","state":{"status":"completed","input":{"operation":"search","query":"Qdrant associative memory #162 Rune spike projection"},"output":"Found 10 matches (BM25-ranked, best first).\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\nIf you need the FULL body (snippets are truncated), Read the path.\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\n\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY-rules-gitea-2026-09-05.md\nScope: projects/global, Type: memory, Score: 17.525\n...New issues #161 (backend harness), #<<162>> (<> <>), #163 (graph <>). FREEZE deep sqlite-vec/graph optimization unless needed for correctness or benchmark baseline. Do NOT adopt KuzuDB (archived Oct 2025). Tech...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f98e46b46ffe7fdgz483s/checkpoint.md\nScope: sessions/ses_-ffe5f98e46b46ffe7fdgz483s, Type: checkpoint, Score: 13.052\n...2. **Storage-frontier sequence (P1, after #148–#152):** #161 (<> backend abstraction + comparative benchmark harness) → #<<162>> (<> <> incl. ColBERT late-interaction reranking) → #163 (temporal graph <>: Neo4j vs FalkorDB, LadybugDB experimental...\n\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 7.072\n...<>/<>_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `<>/graph/graph.sqlite` (`src/<>/graph_<>.py`); no <> dependency...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 6.723\n...Issue-161 OUTWARD research stage — context gathered (stage contract, issue-263 artifact exemplar, <> <>-stack grounding); next: batch DDG SERP discovery on SQLite FTS5/sqlite-vec vs <> vs graph backends...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cabf29bffeBquaPsPsG/checkpoint.md\nScope: sessions/ses_-ffe5f7cabf29bffeBquaPsPsG, Type: checkpoint, Score: 6.205\n...<> has no <> code (confirmed by grep).\n- **C6: Graph substrate family / structural-graph evaluation** — sources [O15]; evidence [E18, E20, E22]; classification APPLICABLE_WITH_ADAPTATION (graph tier exists in graph_<>.py...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fdce6835cffezTlUd3Fwd/warm_neuron_research.md\nScope: sessions/ses_-ffe5fdce6835cffezTlUd3Fwd, Type: free, Score: 5.433\n...<> <> IN COGNITIVE ARCHITECTURES\n\n### How SOAR, ACT-R, and Modern Agents Differ from Keyword Search\n\nThe fundamental difference between <> retrieval and keyword search:\n\n| Property | Keyword Search | <> Retrieval |\n|---|---|---|\n| Match criterion...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 5.279\n...Promote to <>.md ## Architecture decisions when proven cross-session-durable._\n\n- Chose project-<> protocol over skill loading: the stage prompt's contract (receipts, O-IDs, no synthesis) matches the established <>...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c9d2281ffessfv3vUH4/checkpoint.md\nScope: sessions/ses_-ffe5f7c9d2281ffessfv3vUH4, Type: checkpoint, Score: 5.247\n...Read `src/<>/<>_selector.py` in full (511 lines) — see §6 for the attack-relevant facts extracted.\n\nNot yet done: reading <>_store.py / orchestration/benchmark.py / graph_<>.py / v1...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f98e46b46ffe7fdgz483s/checkpoint-directives-spillover.md\nScope: sessions/ses_-ffe5f98e46b46ffe7fdgz483s, Type: checkpoint, Score: 5.120\n...Canonical durable state is the ONLY authority; <>/graph are rebuildable projections. Migrate only on demonstrated benchmark advantage.\n- **ANTI-COMPLEXITY RULES (integrity handoff §10, binding):** a concern is not automatically a subsystem...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB/checkpoint.md\nScope: sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB, Type: checkpoint, Score: 4.496\n...Key load-bearing facts for the classification (from the dossier):\n- <>'s graph surface is a headless SQLite tier at `<>/graph/graph.sqlite` (`src/<>/graph_<>.py`), no external graph...\n","metadata":{"count":10,"truncated":false},"title":"Memory search: 10 results","time":{"start":1788986521895,"end":1788986521950}},"id":"prt_g001a087e7f68e00153b6lF2sM","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e7ceaf001lDCQmjnW4"}} +{"type":"step_finish","timestamp":1788986522011,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e7f961001ucwu6Fl4h","reason":"tool-calls","snapshot":"99d2134cb78c1e678cfc67cd6f3fa9bb974e82c7","messageID":"msg_g001a087e7ceaf001lDCQmjnW4","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":28727,"input":28475,"output":252,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}} +{"type":"step_start","timestamp":1788986529860,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e81842001jS52h02YA","messageID":"msg_g001a087e7f9dd001rZch1d7mk","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"6a4bc2f9a6440ca668c95bb630f37780381ff7af","type":"step-start"}} +{"type":"text","timestamp":1788986530983,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e818ee002sdFh1gYJ8","messageID":"msg_g001a087e7f9dd001rZch1d7mk","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"The closest skill (super-research) doesn't match this bespoke stage contract — prior sessions established a project-memory protocol for OUTWARD stages. Let me pull the project memory and prior stage exemplar to follow the established format.","time":{"start":1788986530030,"end":1788986530981}}} +{"type":"tool_use","timestamp":1788986530993,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"read","callID":"call_caad2e19217244fda593e1dd","state":{"status":"completed","input":{"file_path":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY.md"},"output":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY.md\nfile\n\n1: Topic: Rune research pipeline — headless structured research rounds (stages, stage artifacts, SQLite usage facts) against Gitea issues\n2: # Project memory\n3: _Durable project-level knowledge. Persists across all sessions in this project. Edit only content under italic instructions._\n4: \n5: ## Project context\n6: _What is this project? What's its goal? High-level identity._\n7: \n8: Rune project research pipeline (repo at `/mnt/workdrive/rune/`). Sessions in this project execute structured research rounds against Gitea issues, one stage at a time, dispatched as headless prompts carrying a process capsule (process_id, issue, round, current_stage, allowed_action_now, expected_completion_artifact).\n9: \n10: ## Rules\n11: _Hard constraints from user that every session must respect._\n12: \n13: - Stage prompts are authoritative: obey `allowed_action_now` strictly (e.g., DEFINE forbids codebase analysis and web research).\n14: - Stage artifacts end with the literal line `StageCompleted: ` (or `StageFailed: ` + `Reason:`). Never fabricate a stage outcome.\n15: \n16: ## Architecture decisions\n17: _Major design choices with rationale. The \"why\" matters more than the \"what\" for future sessions._\n18: \n19: (none yet)\n20: \n21: ## Discovered durable knowledge\n22: _Cross-task facts that survive across sessions. Promoted from session checkpoints' §7 when proven durable._\n23: \n24: - `src/rune/memory_selector.py` has a documented KNOWN RANKING BIAS (P1-03 #26 finding 9): merged candidates are ranked by RAW score across mode-dependent calibrated scales (lexical store ~4-6, dense/hybrid RRF ~0-0.13, self-scored 0-1), so cross-source candidate ORDER is biased while abstention is not (each source floored on its own scale; store rows carry `store_scored=True` and are exempt from the selector-side numeric check). Any retrieval-quality benchmark of `memory_store.recall` alone measures the store, not what production surfaces — selector merge order is a separate unmeasured stage. Also: `MAX_CANDIDATES=24`, `DEFAULT_BUDGET=4`, persona lane always runs a second typed query for self/personality, shard routing deliberately bypassed.\n25: - Issue-163 ADVERSARIAL accepted (artifact `research/issue-163/adversarial.json`, harness repo): 13 attacks (A1–A13) settled INN-1's two halves. Survivors: C4 (SURVIVES_WITH_DOWNGRADE — \"bi-temporal E14 1:1\" claim is false; actual = transaction-time append log with same-triple supersession), C5 (SURVIVES_ONLY_IN_CORRECTED_FORM — INN-1's as-of filter is falsified: supersede never writes valid_until so superseded edges are NULL-open and pass the filter forever), C6 (SURVIVES_CONDITIONAL — graph is EMPTY and memory_selector re-floors graph candidates), C9 (SURVIVES_TRACK_ONLY). REJECTED (do NOT resurrect): INN-1#temporal-filter-as-specified. Accepted mitigations M1–M7: M1 fix temporal semantics before any as-of ships ((a) set valid_until=now on supersede at graph_memory.py:206-208, OR (b) transaction-time-only as-of via learned_at+superseded_by chain-walk); M2 normalize --as-of input to now_iso() format before string comparison; M3 FTS5 index = rebuildable projection with documented rebuild + deterministic alias dedup; M4 measure benefit at memory_selector output (floor 0.42), keep connect/stats contract (contracts.py:233-240) and canary bans (canary_p0_04.py:99-105); M5 write-side population precedes read-layer work; M6 describe tier as transaction-time append log, not bi-temporal; M7 future analytics sidecar = rebuildable read-only projection, no independent writes. Falsification tests T1–T6 (T1 kills the INN-1 filter; T2 validates M1a; T3 lexical ≥30% end-to-end; T4 add-bulk 1,000 edges commits-constant; T5 population gate <50 edges ⇒ NO_INNOVATION_WARRANTED; T6 as-of input robustness). Critical assumptions CA1–CA6 (graph empty, valid-time degenerate, timestamps lexicographic only for producer format, benefit measured end-to-end, FTS5 available sqlite 3.53.4, supersession chains short/acyclic but unasserted).\n26: - Issue-163 SYNTHESIS round 1 accepted (artifact `research/issue-163/synthesis.json`, harness repo, 150 lines): selected design = corrected, re-sequenced temporal+lexical query layer over graph.sqlite. Sequence 0–9: (0) pre-flight G4 check — graph source registered in memory_selector.DEFAULT_SOURCES; (1) M1a one-line write fix `UPDATE edges SET status='superseded', superseded_by=?, valid_until=? WHERE id=?` at graph_memory.py:206-208 (valid_until = same now_iso() as the superseding edge; DB empty so backfill free); (2) M2 `normalize_as_of()` (Z/offset/fractional ISO → now_iso() format or ValueError; never raw lexicographic compare); (3) M5 gate — write-side population underway before read-layer ships; (4) C5 `--as-of` bitemporal filter in BOTH edge queries (:254 outgoing, :275 incoming), default no-flag path byte-identical (INV-10); (5) M3 graph_lex FTS5 table + `rebuild-lex` CLI + deterministic alias ORDER BY; (6) kill tests T3/T4 (lexical ≥30% held-out resolution AND floor survival at memory_selector output; add-bulk 1,000 edges commits-constant, >10% regression kills); (7) M4 permanent selector-floor test; (8) M6 docs (\"transaction-time append log with same-triple supersession\", never \"bi-temporal E14 1:1\"; fix dead docstring :37); (9) T5 population gate (<50 edges over pre-committed N weeks ⇒ NO_INNOVATION_WARRANTED). 11 invariants INV-1..11, 11 tests_required, AC1-AC10; rollback = remove flag+tier / DROP graph_lex / M1a stands alone. C4 ships as docs-only characterization (M6); C9 ships as nothing (M7 constraint on future sidecar). Next stage QUALIFY.\n27: - Issue-163 SYNTHESIS round 2 accepted (artifact `research/issue-163/synthesis.json`, harness repo, 178 lines, supersedes round 1 in-place): same design (corrected, re-sequenced temporal+lexical query layer over graph.sqlite, sequence 0–9, INV-1..11, AC1–AC10, M1–M7, T1–T6) with all 3 round-1 QUALIFY findings committed via a new `qualify_refine_findings_resolution` section — (1) graph_lex DDL retracted and replaced: `CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, entity_id UNINDEXED, tokenize='unicode61')` NON-contentless, ONE ROW PER (entity_id, text) over entities.name ∪ aliases.alias (name-row repeats name in alias column), entity_id UNINDEXED column is the SOLE FTS-row→entity mapping (no rowid convention, no join-on-name); maintenance INSERT-only — name-row on entity creation in resolve_entity, alias-row in add_edge ONLY when the aliases INSERT OR IGNORE actually inserted (rowcount==1); NO UPDATE/DELETE anywhere; future deletions route through rebuild-lex; contentless and packed-rowid layouts recorded in rejected_alternatives; (2) N=4 weeks pre-committed for T5 population gate (<50 edges), fixed in INV-5/sequence-step-9/AC9/test_population_gate_harness, may not be re-chosen after observing data; (3) alias sort key pinned: `ORDER BY a.entity_id ASC LIMIT 1` at graph_memory.py:228-231 AND equivalent `ORDER BY a.entity_id ASC` at :147-151 (both sites, else INV-8 unverifiable). New tests added: test_graph_lex_write_path_parity, test_graph_lex_rebuild_roundtrip, test_alias_resolution_deterministic. Sequence step 0 = G4 pre-flight (graph source registered in memory_selector.DEFAULT_SOURCES; flagged \"dormant source\" risk unverified this process). Next stage: QUALIFY round 2 (re-review of the same artifact).\n28: - task_tree.json round-loop shape (issue-163): `completed_stages` records the actual dispatch history (…SYNTHESIS, QUALIFY, SYNTHESIS), so a REFINE loop shows as duplicated stage names with the `round` field lagging (still \"1\" in round 2); `current_stage` is authoritative for where the pipeline is. Stage artifacts on disk (synthesis.json, qualify.json, etc.) are round-slot files overwritten in place, not append logs.\n29: - QUALIFY round-2 re-review convention (observed, pending confirmation): the stage prompt re-issues the same 5-check review against the rewritten artifact; the prior qualify.json slot is overwritten with the new verdict; verify each round-1 finding's claimed resolution actually lands in the artifact body (not just in the resolution section) before confirming PROCEED.\n30: - QUALIFY stage dispatch convention (confirmed identical on issue-161 and issue-163): judge ONLY implementation-readiness of the accepted synthesis.json — 5 checks (interface ambiguity a coder couldn't implement without guessing; implementation-sequence order; acceptance-criteria observability; authority-boundary contradictions; rollback reality). Do NOT re-research, re-litigate the design, or propose better approaches. Output `research/issue-/qualify.json` with keys `outcome` (\"PROCEED\"|\"REFINE_SYNTHESIS\") + `findings` (list of strings; confirmations if PROCEED, concrete gaps if REFINE) + optional `reopen` {reason, return_to, new_blocking_fact, why_previous_gate_could_not_reasonably_have_caught_it, required_fix}, used sparingly.\n31: - Issue-163 operational pointers: stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT `/mnt/workdrive/research-stage/` — that path doesn't apply to new rounds); known decoy `graph_memory.py` files on this machine: `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py` — the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines); `StageCompleted:` marker convention for JSON artifacts unconfirmed (innovation.json and round-1 synthesis.json carried none).\n32: - Issue-310 (ADAPT-1 B1 telemetry) OUTWARD evidence (in progress at checkpoint): root cause chain confirmed repo-side — StageAttempt window markers (mark_model/mark_tool/mark_gate/mark_persistence, stage_telemetry.py:83-112) are invoked ONLY in tests/test_stage_telemetry.py; the production executor scripts/research_stage_executor.py:87-91 constructs StageAttempt+TelemetryLedger but never opens windows, so model_time_s/tool_time_s are structurally 0 (empirically confirmed in research/issue-310/telemetry/attempts.jsonl: both ISSUE/INWARD attempts COMPLETED with window_counts all 0). Distinguish the two telemetry systems: per-inference traces (src/rune/inference_trace.py, model_time_ms from Ollama total_duration, POPULATED in production via rune_operator.py:1574 + local_tool_runtime.py accumulators) vs per-stage attempts (scripts/stage_telemetry.py, NEVER populated). New harness receipts convention: per-stage event logs at `research/issue-/stage_.events.jsonl` + `.consumed_issue---` idempotency markers.\n33: - See MEMORY-pipeline-history.md (15 entries) — pipeline stage conventions, OUTWARD discovery/reading techniques, SQLite/memory-stack grounding facts, issue-262, and issue-161/issue-163 early-stage artifacts (OUTWARD → APPLICABILITY → INNOVATION) for completed rounds.\n34: \n35: ### Discovered\n36: - Issue-311 OUTWARD (ADAPT-1 B2 wiring-status/architecture-truth report generator, sub of #158) discovery+reading done at 2026-09-09 (artifact write pending at checkpoint): 7 DuckDuckGo SERP queries (1 empty), O1–O7 sources classified, 4 read (import-linter, dependency-cruiser, vulture, ArchUnit user guide). Key finding: all surveyed architecture-truth tooling is STATIC (import graphs/bytecode/rules); NO tool covers runtime \"Live caller/Causal path\" truth — #158's table needs registry/entry-point instrumentation, not an off-the-shelf linter. Repo receipts: `config/v1_authority_map.json` verified (26538 bytes, dispositions/responsibilities); `scripts/gen_prd_state.py` = existing generated-report pattern; `orchestration/benchmark.py` fingerprint():22 = reporting pattern; **\"walkman pattern\" does NOT exist** in `/mnt/workdrive/rune` or harness repo (grep empty) — #311's \"module scan (walkman pattern exists)\" dependency claim is stale; record as limitation. Issue #158 acceptance (Gitea API): report columns Subsystem/Defined/Live caller/Causal path/Tests/Canary/Benchmark; must identify ≥1 deliberately-disconnected test subsystem as not-live; generated from import graphs/registries, no hand-maintained truth; CI or on-demand.\n37: - Issue-311 round (ADAPT-1 B2 wiring-status/architecture-truth report generator, sub of #158) opened in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-311/`: deterministic DEFINE contract (ISSUE→INWARD→OUTWARD→ADVERSARIAL→INNOVATION→SYNTHESIS→QUALIFY, APPLICABILITY skipped), ISSUE+INWARD auto-completed with keyword claims I1–I5, OUTWARD pending, no outward artifact yet (state at 2026-02 checkpoint).\n38: \n39: ### Dead ends\n40: - (none)\n\n(End of file - total 40 lines)\n","metadata":{"preview":"Topic: Rune research pipeline — headless structured research rounds (stages, stage artifacts, SQLite usage facts) against Gitea issues\n# Project memory\n_Durable project-level knowledge. Persists across all sessions in this project. Edit only content under italic instructions._\n\n## Project context\n_What is this project? What's its goal? High-level identity._\n\nRune project research pipeline (repo at `/mnt/workdrive/rune/`). Sessions in this project execute structured research rounds against Gitea issues, one stage at a time, dispatched as headless prompts carrying a process capsule (process_id, issue, round, current_stage, allowed_action_now, expected_completion_artifact).\n\n## Rules\n_Hard constraints from user that every session must respect._\n\n- Stage prompts are authoritative: obey `allowed_action_now` strictly (e.g., DEFINE forbids codebase analysis and web research).\n- Stage artifacts end with the literal line `StageCompleted: ` (or `StageFailed: ` + `Reason:`). Never fabricate a stage outcome.\n\n## Architecture decisions\n_Major design choices with rationale. The \"why\" matters more than the \"what\" for future sessions._\n\n(none yet)\n","truncated":false,"loaded":[]},"title":"../../../home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY.md","time":{"start":1788986530983,"end":1788986530992}},"id":"prt_g001a087e819900014WJxLrcKA","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e7f9dd001rZch1d7mk"}} +{"type":"tool_use","timestamp":1788986531024,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"memory","callID":"call_f769394bdc5b44d6b8ecc7b9","state":{"status":"completed","input":{"operation":"search","query":"OUTWARD stage protocol O-IDs receipts issue-263 exemplar"},"output":"Found 10 matches (BM25-ranked, best first).\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\nIf you need the FULL body (snippets are truncated), Read the path.\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 28.303\n...Assign <>-<>, classify, select bounded set, read via webfetch, then write the <> artifact (matching `research/<>-<<263>>/<>.json` format) into `/mnt/workdrive/research-<>/research/<>-161/` ending with...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c/checkpoint.md\nScope: sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c, Type: checkpoint, Score: 23.068\n...<> <> for process `research-<<263>>-r1` (<> <<263>>: \"OR3 vertical acceptance: <> unified <> — sqlite WAL + concurrency in Python\"). MUST: real web searches with query/source <>, <>-numbered source <>...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 17.110\n...memory <> over skill loading: the <> prompt's contract (<>, <>-<>, no synthesis) matches the established Rune pipeline <> <> exactly, so the DuckDuckGo-HTML-SERP technique and <>-161...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782475a4ffegE8Ix6aZJ/checkpoint.md\nScope: sessions/ses_-ffe5f782475a4ffegE8Ix6aZJ, Type: checkpoint, Score: 16.898\n...headless Rune research-pipeline <> <> dispatch for <>-311. Objective: discover and classify candidate external sources — <> real searches/discovery calls, preserve query/tool <>, assign stable source <> (O1, O2...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR/checkpoint.md\nScope: sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR, Type: checkpoint, Score: 15.867\n...reads already executed (<> are in `/mnt/workdrive/issue161-harness/research/<>-310/<>_<>.events.jsonl`). Evidence requirement for the subquestion is already satisfied (see §7); per <> \"Continue into further...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint-outward-discovery.md\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 15.196\n...this is a Rune research pipeline headless <> dispatch (unified 7-<> pipeline; <> = discovery + reading merged, <>-numbered <>, artifact ends `StageCompleted: <>`).\n- REQUIRED SOURCE CLASSES (from the dispatch): repo, external.\n\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f826d4b3affeptScH2iSt/checkpoint-handoff-research-loop.md\nScope: sessions/ses_-ffe5f826d4b3affeptScH2iSt, Type: checkpoint, Score: 14.087\n...agent_<>.<>_valid (receipt-level) + T6 artifact-level\n- §10 <> from event stream, NEVER model self-report → SHIPPED #222 (build_receipt)\n- §11 INNOVATION candidates must derive_from evidence <>; ≥1 candidate...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint.md\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 13.693\n...Rollback: /usr/local/bin/gitea-1.27.2.bak.\n- research/<>-161/ + <>-163/ + <>-<<263>>/ artifact dirs — historical evidence, preserve. #161 legacy ledger render preview at /tmp/ledger161/<>.json.\n- Pi...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c9d2281ffessfv3vUH4/checkpoint.md\nScope: sessions/ses_-ffe5f7c9d2281ffessfv3vUH4, Type: checkpoint, Score: 13.405\n...Items here are candidates for promotion to projects//MEMORY.md if they prove durable._\n\n- <>-161 round uses the newer pipeline shape: unified <> (<>-numbered sources) + APPLICABILITY <> producing `applicability...\n\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY-discovered-pipeline-build-history-2026-09-09.md\nScope: projects/global, Type: memory, Score: 13.183\n...deterministic receipt reconciliation — replay <>_<<>>.events.jsonl through ResearchEventRelay._classify_event to extract real <> without re-running MiMo.\n- **<> GATE REQUIREMENTS (canonical, enforced):** unified artifact discovery{queries,sources_discovered,source...\n","metadata":{"count":10,"truncated":false},"title":"Memory search: 10 results","time":{"start":1788986530982,"end":1788986531023}},"id":"prt_g001a087e8190d001xq59zsq41","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e7f9dd001rZch1d7mk"}} +{"type":"step_finish","timestamp":1788986531047,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e81cd00018gD5kZXe2","reason":"tool-calls","snapshot":"3284dabc6e5dfd9f0c738a1c990c076d2dcbff02","messageID":"msg_g001a087e7f9dd001rZch1d7mk","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":30464,"input":12658,"output":206,"reasoning":0,"cache":{"write":0,"read":17600}},"cost":0}} +{"type":"step_start","timestamp":1788986536534,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e83253001ABFA7jcRQ","messageID":"msg_g001a087e81d04001Rg024dFcG","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"a493155a7847a4757cc681720d21087484c88d51","type":"step-start"}} +{"type":"tool_use","timestamp":1788986537431,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"read","callID":"call_aed0b93291bc467d966d0bc2","state":{"status":"completed","input":{"file_path":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md"},"output":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\nfile\n\n1: # Memory spillover: pipeline history & completed early-stage artifacts\n2: _Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\n3: \n4: ## Entries\n5: \n6: - Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\n7: - Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \"the research question\" without restating it — locate the question file before classifying.\n8: - Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \"OUTWARD_DISCOVERY executor\"); distinct from the subject repo `/mnt/workdrive/rune/`.\n9: - OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \"discover URLs, classify sources, don't read full sources\" stage contract.\n10: - OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\n11: - Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\n12: - Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \"skipped\" while the unified stage still requires \"≥2 unique URLs\".\n13: - Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \"vs Qdrant vs graph backends\" part of the question is comparative research, not existing code.\n14: - Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\n15: - Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\n16: - Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\"PROCEED\"|\"REFINE_SYNTHESIS\") + `findings` (+ optional `reopen`).\n17: \n18: - Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\n19: \n20: - Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \"temporal+lexical query layer over graph.sqlite\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).\n21: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT, single merged candidate INN-1 \"temporal+lexical query layer over graph.sqlite\" (composition_of C5+C6+existing C4 schema; novelty=compositional; zero schema/backend change; embedding tier deliberately omitted). kill_test = two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t; lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files on this machine: `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py`; the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json carries NO `StageCompleted:` line — convention for JSON artifacts unconfirmed. (An earlier near-duplicate INNOVATION entry further below predates this one; both agree on facts.) (1) add_edge (graph_memory.py:200-204) writes the SAME now_iso() into valid_from AND learned_at and always NULL valid_until — the \"bi-temporal\" schema has only ever carried one effective time axis; an as-of filter degenerates to transaction-time-only unless a distinct valid-time source is added. (2) Supersession is triple-local: only identical (subj_id, pred, obj_id) active edges are superseded (:184-186); reworded contradictions across predicates/entities co-exist active. (3) Only idx_edges_subj/idx_edges_obj exist (:117-118) — no index serves temporal or status filters. (4) query() BFS traverses outgoing edges only (:257-267); incoming edges appended at depth 0 but never expanded. (5) Entity resolution is ... (line truncated to 2000 chars)\n22: \n23: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT with single merged candidate INN-1 \"temporal+lexical query layer over graph.sqlite\" (composition_of C5+C6+existing C4 schema; novelty=compositional; no schema/backend change; embedding tier deliberately omitted). kill_test has two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t (negligible superseded accumulation); lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files exist on this machine — `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py` — the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json does NOT end with a `StageCompleted:` line; convention for JSON artifacts unconfirmed.\n\n(End of file - total 23 lines)\n","metadata":{"preview":"# Memory spillover: pipeline history & completed early-stage artifacts\n_Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\n\n## Entries\n\n- Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\n- Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \"the research question\" without restating it — locate the question file before classifying.\n- Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \"OUTWARD_DISCOVERY executor\"); distinct from the subject repo `/mnt/workdrive/rune/`.\n- OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \"discover URLs, classify sources, don't read full sources\" stage contract.\n- OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\n- Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\n- Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \"skipped\" while the unified stage still requires \"≥2 unique URLs\".\n- Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \"vs Qdrant vs graph backends\" part of the question is comparative research, not existing code.\n- Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\n- Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\n- Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\"PROCEED\"|\"REFINE_SYNTHESIS\") + `findings` (+ optional `reopen`).\n\n- Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\n\n- Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \"temporal+lexical query layer over graph.sqlite\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).","truncated":false,"loaded":[]},"title":"../../../home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md","time":{"start":1788986537331,"end":1788986537429}},"id":"prt_g001a087e83255001xDMhpUhWN","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e81d04001Rg024dFcG"}} +{"type":"step_finish","timestamp":1788986537742,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e835d6001EHCrjwZ3N","reason":"tool-calls","snapshot":"982c09e4a37cf61612e923d16e5ee29ecb5ff4d2","messageID":"msg_g001a087e81d04001Rg024dFcG","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":35172,"input":4847,"output":53,"reasoning":0,"cache":{"write":0,"read":30272}},"cost":0}} +{"type":"step_start","timestamp":1788986551696,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e86d8e0014BjDz9uA8","messageID":"msg_g001a087e838ae001WWkZOyLBR","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"7c8cafa779a3b69a7bbaf36203b72488a470ab15","type":"step-start"}} +{"type":"text","timestamp":1788986553677,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e87041002fASaklhmu","messageID":"msg_g001a087e838ae001WWkZOyLBR","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"Grounding established: this is the Rune research pipeline's unified OUTWARD stage for issue #162 (Qdrant spike), same protocol as issue-161/311/313 — real searches with receipts, O-numbered sources, evidence claims, no synthesis, artifact ending `StageCompleted: OUTWARD`. Let me check the stage state in the harness repo and the issue itself.","time":{"start":1788986552385,"end":1788986553673}}} +{"type":"tool_use","timestamp":1788986553981,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_4ed31c5890cc4395b6e65af7","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/issue161-harness/research/issue-162/ 2>/dev/null; cat /mnt/workdrive/issue161-harness/research/issue-162/task_tree.json 2>/dev/null | head -60","description":"Check issue-162 research stage state"},"output":"stage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n{\n \"issue\": 162,\n \"round\": 1,\n \"current_stage\": \"OUTWARD\",\n \"completed_stages\": [\n \"ISSUE\",\n \"INWARD\"\n ],\n \"artifacts\": {\n \"ISSUE\": {\n \"contract\": {\n \"issue\": 162,\n \"title\": \"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"deterministic\": true\n },\n \"INWARD\": {\n \"context\": {\n \"keywords\": [\n \"qdrant\",\n \"associative\",\n \"memory\",\n \"projection\",\n \"spike\",\n \"dense\",\n \"sparse\",\n \"multivector\",\n \"colbert\",\n \"late\",\n \"interaction\",\n \"reranking\",\n \"plain\",\n \"summary\",\n \"architecture_frontier_handoff\",\n \"highest\",\n","metadata":{"output":"stage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n{\n \"issue\": 162,\n \"round\": 1,\n \"current_stage\": \"OUTWARD\",\n \"completed_stages\": [\n \"ISSUE\",\n \"INWARD\"\n ],\n \"artifacts\": {\n \"ISSUE\": {\n \"contract\": {\n \"issue\": 162,\n \"title\": \"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"deterministic\": true\n },\n \"INWARD\": {\n \"context\": {\n \"keywords\": [\n \"qdrant\",\n \"associative\",\n \"memory\",\n \"projection\",\n \"spike\",\n \"dense\",\n \"sparse\",\n \"multivector\",\n \"colbert\",\n \"late\",\n \"interaction\",\n \"reranking\",\n \"plain\",\n \"summary\",\n \"architecture_frontier_handoff\",\n \"highest\",\n","exit":0,"description":"Check issue-162 research stage state","truncated":false},"title":"Check issue-162 research stage state","time":{"start":1788986553977,"end":1788986553980}},"id":"prt_g001a087e8704f0010b38Ld1TT","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e838ae001WWkZOyLBR"}} +{"type":"tool_use","timestamp":1788986553984,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_545636ccd237428faa036cde","state":{"status":"completed","input":{"command":"curl -s \"http://localhost:3000/api/v1/repos/nick/rune/issues/162\" 2>/dev/null | head -c 1500; echo; curl -s \"http://localhost:3000/api/v1/repos/\" -o /dev/null -w \"%{http_code}\\n\" 2>/dev/null","description":"Try Gitea API for issue 162"},"output":"\n000\n","metadata":{"output":"\n000\n","exit":7,"description":"Try Gitea API for issue 162","truncated":false},"title":"Try Gitea API for issue 162","time":{"start":1788986553981,"end":1788986553983}},"id":"prt_g001a087e8705d001tBRa6cwX4","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e838ae001WWkZOyLBR"}} +{"type":"step_finish","timestamp":1788986554156,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e87680001tREL1xRpJ","reason":"tool-calls","snapshot":"b0498d5287e0e03c130a59d97d301726d5d535f9","messageID":"msg_g001a087e838ae001WWkZOyLBR","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":38682,"input":2873,"output":673,"reasoning":0,"cache":{"write":0,"read":35136}},"cost":0}} +{"type":"step_start","timestamp":1788986560087,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e88e55001IYqYQelsU","messageID":"msg_g001a087e877ee0016PAppIuDz","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"23b9675157063d87bb8d458407705b5e28b4fe85","type":"step-start"}} +{"type":"tool_use","timestamp":1788986561855,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_3ce722e73e55437c8d695e49","state":{"status":"completed","input":{"command":"sed -n '60,200p' /mnt/workdrive/issue161-harness/research/issue-162/task_tree.json","description":"Read rest of task_tree.json"},"output":" \"highest\",\n \"value\",\n \"retrieval\",\n \"experiment\",\n \"qdrant\"\n ],\n \"umbrella_matches\": [\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_store\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_consolidation\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_env\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"query_memory\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"graph_memory\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_selector\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"retrieval_benchmark\",\n \"keyword\": \"retrieval\"\n }\n ],\n \"candidate_files\": [\n \"src/rune/memory_store.py\",\n \"src/rune/sleep_time.py\",\n \"src/rune/canary_p1_07.py\",\n \"src/rune/curator.py\",\n \"src/rune/rune_tui.py\",\n \"src/rune/warm_router.py\",\n \"src/rune/abstraction.py\",\n \"src/rune/canary_full_system.py\",\n \"src/rune/canary_p1_01.py\",\n \"src/rune/memory_consolidation.py\",\n \"src/rune/rune_operator.py\",\n \"src/rune/cache.py\",\n \"src/rune/canary_p1_02.py\",\n \"src/rune/canary_p1_06.py\",\n \"src/rune/canary_tui_drift.py\"\n ],\n \"candidate_tests\": [\n \"tests/test_p1_03_memory_selection.py\",\n \"tests/test_p1_07_runtime_objects.py\",\n \"tests/test_retrieval_benchmark.py\",\n \"tests/test_p0_07_inference_trace.py\",\n \"tests/test_p1_01_capability_authority.py\",\n \"tests/test_synthesis_executor.py\",\n \"tests/test_context_broker.py\",\n \"tests/test_failure_auto_filer.py\",\n \"tests/test_p1_02_capsule_authority.py\",\n \"tests/test_p1_06_model_routing.py\"\n ],\n \"define_contract\": {\n \"issue\": 162,\n \"title\": \"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"mimo_needed\": false\n },\n \"deterministic\": true,\n \"claims\": [\n {\n \"id\": \"I1\",\n \"claim\": \"Relevant file: src/rune/memory_store.py\",\n \"evidence\": [\n \"src/rune/memory_store.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I2\",\n \"claim\": \"Relevant file: src/rune/sleep_time.py\",\n \"evidence\": [\n \"src/rune/sleep_time.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I3\",\n \"claim\": \"Relevant file: src/rune/canary_p1_07.py\",\n \"evidence\": [\n \"src/rune/canary_p1_07.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I4\",\n \"claim\": \"Relevant file: src/rune/curator.py\",\n \"evidence\": [\n \"src/rune/curator.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I5\",\n \"claim\": \"Relevant file: src/rune/rune_tui.py\",\n \"evidence\": [\n","metadata":{"output":" \"highest\",\n \"value\",\n \"retrieval\",\n \"experiment\",\n \"qdrant\"\n ],\n \"umbrella_matches\": [\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_store\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_consolidation\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_env\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"query_memory\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"graph_memory\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"memory_selector\",\n \"keyword\": \"memory\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"retrieval_benchmark\",\n \"keyword\": \"retrieval\"\n }\n ],\n \"candidate_files\": [\n \"src/rune/memory_store.py\",\n \"src/rune/sleep_time.py\",\n \"src/rune/canary_p1_07.py\",\n \"src/rune/curator.py\",\n \"src/rune/rune_tui.py\",\n \"src/rune/warm_router.py\",\n \"src/rune/abstraction.py\",\n \"src/rune/canary_full_system.py\",\n \"src/rune/canary_p1_01.py\",\n \"src/rune/memory_consolidation.py\",\n \"src/rune/rune_operator.py\",\n \"src/rune/cache.py\",\n \"src/rune/canary_p1_02.py\",\n \"src/rune/canary_p1_06.py\",\n \"src/rune/canary_tui_drift.py\"\n ],\n \"candidate_tests\": [\n \"tests/test_p1_03_memory_selection.py\",\n \"tests/test_p1_07_runtime_objects.py\",\n \"tests/test_retrieval_benchmark.py\",\n \"tests/test_p0_07_inference_trace.py\",\n \"tests/test_p1_01_capability_authority.py\",\n \"tests/test_synthesis_executor.py\",\n \"tests/test_context_broker.py\",\n \"tests/test_failure_auto_filer.py\",\n \"tests/test_p1_02_capsule_authority.py\",\n \"tests/test_p1_06_model_routing.py\"\n ],\n \"define_contract\": {\n \"issue\": 162,\n \"title\": \"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"mimo_needed\": false\n },\n \"deterministic\": true,\n \"claims\": [\n {\n \"id\": \"I1\",\n \"claim\": \"Relevant file: src/rune/memory_store.py\",\n \"evidence\": [\n \"src/rune/memory_store.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I2\",\n \"claim\": \"Relevant file: src/rune/sleep_time.py\",\n \"evidence\": [\n \"src/rune/sleep_time.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I3\",\n \"claim\": \"Relevant file: src/rune/canary_p1_07.py\",\n \"evidence\": [\n \"src/rune/canary_p1_07.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I4\",\n \"claim\": \"Relevant file: src/rune/curator.py\",\n \"evidence\": [\n \"src/rune/curator.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I5\",\n \"claim\": \"Relevant file: src/rune/rune_tui.py\",\n \"evidence\": [\n","exit":0,"description":"Read rest of task_tree.json","truncated":false},"title":"Read rest of task_tree.json","time":{"start":1788986561852,"end":1788986561854}},"id":"prt_g001a087e892be001YXNWYzCqu","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e877ee0016PAppIuDz"}} +{"type":"tool_use","timestamp":1788986561859,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_26cf80e213d94b749d99c95c","state":{"status":"completed","input":{"command":"tail -5 /mnt/workdrive/issue161-harness/research/issue-162/stage_OUTWARD.events.jsonl 2>/dev/null; wc -l /mnt/workdrive/issue161-harness/research/issue-162/stage_OUTWARD.events.jsonl","description":"Check event log for prior OUTWARD attempts"},"output":"{\"type\":\"text\",\"timestamp\":1788986553677,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"id\":\"prt_g001a087e87041002fASaklhmu\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"type\":\"text\",\"text\":\"Grounding established: this is the Rune research pipeline's unified OUTWARD stage for issue #162 (Qdrant spike), same protocol as issue-161/311/313 — real searches with receipts, O-numbered sources, evidence claims, no synthesis, artifact ending `StageCompleted: OUTWARD`. Let me check the stage state in the harness repo and the issue itself.\",\"time\":{\"start\":1788986552385,\"end\":1788986553673}}}\n{\"type\":\"tool_use\",\"timestamp\":1788986553981,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_4ed31c5890cc4395b6e65af7\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"ls /mnt/workdrive/issue161-harness/research/issue-162/ 2>/dev/null; cat /mnt/workdrive/issue161-harness/research/issue-162/task_tree.json 2>/dev/null | head -60\",\"description\":\"Check issue-162 research stage state\"},\"output\":\"stage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n{\\n \\\"issue\\\": 162,\\n \\\"round\\\": 1,\\n \\\"current_stage\\\": \\\"OUTWARD\\\",\\n \\\"completed_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\"\\n ],\\n \\\"artifacts\\\": {\\n \\\"ISSUE\\\": {\\n \\\"contract\\\": {\\n \\\"issue\\\": 162,\\n \\\"title\\\": \\\"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\",\\n \\\"question_type\\\": \\\"mixed\\\",\\n \\\"sub_questions\\\": [\\n \\\"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\"\\n ],\\n \\\"required_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\",\\n \\\"OUTWARD\\\",\\n \\\"ADVERSARIAL\\\",\\n \\\"INNOVATION\\\",\\n \\\"SYNTHESIS\\\",\\n \\\"QUALIFY\\\"\\n ],\\n \\\"skipped_stages\\\": {\\n \\\"APPLICABILITY\\\": \\\"No OUTWARD research to ground against Rune.\\\"\\n },\\n \\\"evidence_classes\\\": [\\n \\\"repo\\\",\\n \\\"external\\\"\\n ],\\n \\\"sufficient_evidence\\\": \\\"\\\\u22652 code-path citations for INWARD; \\\\u22652 unique URLs for OUTWARD (if required)\\\",\\n \\\"expected_artifact\\\": \\\"synthesis.json\\\",\\n \\\"ambiguous\\\": false,\\n \\\"needs_mimo\\\": false,\\n \\\"source\\\": \\\"deterministic\\\"\\n },\\n \\\"deterministic\\\": true\\n },\\n \\\"INWARD\\\": {\\n \\\"context\\\": {\\n \\\"keywords\\\": [\\n \\\"qdrant\\\",\\n \\\"associative\\\",\\n \\\"memory\\\",\\n \\\"projection\\\",\\n \\\"spike\\\",\\n \\\"dense\\\",\\n \\\"sparse\\\",\\n \\\"multivector\\\",\\n \\\"colbert\\\",\\n \\\"late\\\",\\n \\\"interaction\\\",\\n \\\"reranking\\\",\\n \\\"plain\\\",\\n \\\"summary\\\",\\n \\\"architecture_frontier_handoff\\\",\\n \\\"highest\\\",\\n\",\"metadata\":{\"output\":\"stage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n{\\n \\\"issue\\\": 162,\\n \\\"round\\\": 1,\\n \\\"current_stage\\\": \\\"OUTWARD\\\",\\n \\\"completed_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\"\\n ],\\n \\\"artifacts\\\": {\\n \\\"ISSUE\\\": {\\n \\\"contract\\\": {\\n \\\"issue\\\": 162,\\n \\\"title\\\": \\\"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\",\\n \\\"question_type\\\": \\\"mixed\\\",\\n \\\"sub_questions\\\": [\\n \\\"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\"\\n ],\\n \\\"required_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\",\\n \\\"OUTWARD\\\",\\n \\\"ADVERSARIAL\\\",\\n \\\"INNOVATION\\\",\\n \\\"SYNTHESIS\\\",\\n \\\"QUALIFY\\\"\\n ],\\n \\\"skipped_stages\\\": {\\n \\\"APPLICABILITY\\\": \\\"No OUTWARD research to ground against Rune.\\\"\\n },\\n \\\"evidence_classes\\\": [\\n \\\"repo\\\",\\n \\\"external\\\"\\n ],\\n \\\"sufficient_evidence\\\": \\\"\\\\u22652 code-path citations for INWARD; \\\\u22652 unique URLs for OUTWARD (if required)\\\",\\n \\\"expected_artifact\\\": \\\"synthesis.json\\\",\\n \\\"ambiguous\\\": false,\\n \\\"needs_mimo\\\": false,\\n \\\"source\\\": \\\"deterministic\\\"\\n },\\n \\\"deterministic\\\": true\\n },\\n \\\"INWARD\\\": {\\n \\\"context\\\": {\\n \\\"keywords\\\": [\\n \\\"qdrant\\\",\\n \\\"associative\\\",\\n \\\"memory\\\",\\n \\\"projection\\\",\\n \\\"spike\\\",\\n \\\"dense\\\",\\n \\\"sparse\\\",\\n \\\"multivector\\\",\\n \\\"colbert\\\",\\n \\\"late\\\",\\n \\\"interaction\\\",\\n \\\"reranking\\\",\\n \\\"plain\\\",\\n \\\"summary\\\",\\n \\\"architecture_frontier_handoff\\\",\\n \\\"highest\\\",\\n\",\"exit\":0,\"description\":\"Check issue-162 research stage state\",\"truncated\":false},\"title\":\"Check issue-162 research stage state\",\"time\":{\"start\":1788986553977,\"end\":1788986553980}},\"id\":\"prt_g001a087e8704f0010b38Ld1TT\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788986553984,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_545636ccd237428faa036cde\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"curl -s \\\"http://localhost:3000/api/v1/repos/nick/rune/issues/162\\\" 2>/dev/null | head -c 1500; echo; curl -s \\\"http://localhost:3000/api/v1/repos/\\\" -o /dev/null -w \\\"%{http_code}\\\\n\\\" 2>/dev/null\",\"description\":\"Try Gitea API for issue 162\"},\"output\":\"\\n000\\n\",\"metadata\":{\"output\":\"\\n000\\n\",\"exit\":7,\"description\":\"Try Gitea API for issue 162\",\"truncated\":false},\"title\":\"Try Gitea API for issue 162\",\"time\":{\"start\":1788986553981,\"end\":1788986553983}},\"id\":\"prt_g001a087e8705d001tBRa6cwX4\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788986554156,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"id\":\"prt_g001a087e87680001tREL1xRpJ\",\"reason\":\"tool-calls\",\"snapshot\":\"b0498d5287e0e03c130a59d97d301726d5d535f9\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"type\":\"step-finish\",\"tokens\":{\"total\":38682,\"input\":2873,\"output\":673,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":35136}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788986560087,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"id\":\"prt_g001a087e88e55001IYqYQelsU\",\"messageID\":\"msg_g001a087e877ee0016PAppIuDz\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"snapshot\":\"23b9675157063d87bb8d458407705b5e28b4fe85\",\"type\":\"step-start\"}}\n19 /mnt/workdrive/issue161-harness/research/issue-162/stage_OUTWARD.events.jsonl\n","metadata":{"output":"{\"type\":\"text\",\"timestamp\":1788986553677,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"id\":\"prt_g001a087e87041002fASaklhmu\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"type\":\"text\",\"text\":\"Grounding established: this is the Rune research pipeline's unified OUTWARD stage for issue #162 (Qdrant spike), same protocol as issue-161/311/313 — real searches with receipts, O-numbered sources, evidence claims, no synthesis, artifact ending `StageCompleted: OUTWARD`. Let me check the stage state in the harness repo and the issue itself.\",\"time\":{\"start\":1788986552385,\"end\":1788986553673}}}\n{\"type\":\"tool_use\",\"timestamp\":1788986553981,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_4ed31c5890cc4395b6e65af7\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"ls /mnt/workdrive/issue161-harness/research/issue-162/ 2>/dev/null; cat /mnt/workdrive/issue161-harness/research/issue-162/task_tree.json 2>/dev/null | head -60\",\"description\":\"Check issue-162 research stage state\"},\"output\":\"stage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n{\\n \\\"issue\\\": 162,\\n \\\"round\\\": 1,\\n \\\"current_stage\\\": \\\"OUTWARD\\\",\\n \\\"completed_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\"\\n ],\\n \\\"artifacts\\\": {\\n \\\"ISSUE\\\": {\\n \\\"contract\\\": {\\n \\\"issue\\\": 162,\\n \\\"title\\\": \\\"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\",\\n \\\"question_type\\\": \\\"mixed\\\",\\n \\\"sub_questions\\\": [\\n \\\"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\"\\n ],\\n \\\"required_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\",\\n \\\"OUTWARD\\\",\\n \\\"ADVERSARIAL\\\",\\n \\\"INNOVATION\\\",\\n \\\"SYNTHESIS\\\",\\n \\\"QUALIFY\\\"\\n ],\\n \\\"skipped_stages\\\": {\\n \\\"APPLICABILITY\\\": \\\"No OUTWARD research to ground against Rune.\\\"\\n },\\n \\\"evidence_classes\\\": [\\n \\\"repo\\\",\\n \\\"external\\\"\\n ],\\n \\\"sufficient_evidence\\\": \\\"\\\\u22652 code-path citations for INWARD; \\\\u22652 unique URLs for OUTWARD (if required)\\\",\\n \\\"expected_artifact\\\": \\\"synthesis.json\\\",\\n \\\"ambiguous\\\": false,\\n \\\"needs_mimo\\\": false,\\n \\\"source\\\": \\\"deterministic\\\"\\n },\\n \\\"deterministic\\\": true\\n },\\n \\\"INWARD\\\": {\\n \\\"context\\\": {\\n \\\"keywords\\\": [\\n \\\"qdrant\\\",\\n \\\"associative\\\",\\n \\\"memory\\\",\\n \\\"projection\\\",\\n \\\"spike\\\",\\n \\\"dense\\\",\\n \\\"sparse\\\",\\n \\\"multivector\\\",\\n \\\"colbert\\\",\\n \\\"late\\\",\\n \\\"interaction\\\",\\n \\\"reranking\\\",\\n \\\"plain\\\",\\n \\\"summary\\\",\\n \\\"architecture_frontier_handoff\\\",\\n \\\"highest\\\",\\n\",\"metadata\":{\"output\":\"stage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n{\\n \\\"issue\\\": 162,\\n \\\"round\\\": 1,\\n \\\"current_stage\\\": \\\"OUTWARD\\\",\\n \\\"completed_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\"\\n ],\\n \\\"artifacts\\\": {\\n \\\"ISSUE\\\": {\\n \\\"contract\\\": {\\n \\\"issue\\\": 162,\\n \\\"title\\\": \\\"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\",\\n \\\"question_type\\\": \\\"mixed\\\",\\n \\\"sub_questions\\\": [\\n \\\"Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\\\"\\n ],\\n \\\"required_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\",\\n \\\"OUTWARD\\\",\\n \\\"ADVERSARIAL\\\",\\n \\\"INNOVATION\\\",\\n \\\"SYNTHESIS\\\",\\n \\\"QUALIFY\\\"\\n ],\\n \\\"skipped_stages\\\": {\\n \\\"APPLICABILITY\\\": \\\"No OUTWARD research to ground against Rune.\\\"\\n },\\n \\\"evidence_classes\\\": [\\n \\\"repo\\\",\\n \\\"external\\\"\\n ],\\n \\\"sufficient_evidence\\\": \\\"\\\\u22652 code-path citations for INWARD; \\\\u22652 unique URLs for OUTWARD (if required)\\\",\\n \\\"expected_artifact\\\": \\\"synthesis.json\\\",\\n \\\"ambiguous\\\": false,\\n \\\"needs_mimo\\\": false,\\n \\\"source\\\": \\\"deterministic\\\"\\n },\\n \\\"deterministic\\\": true\\n },\\n \\\"INWARD\\\": {\\n \\\"context\\\": {\\n \\\"keywords\\\": [\\n \\\"qdrant\\\",\\n \\\"associative\\\",\\n \\\"memory\\\",\\n \\\"projection\\\",\\n \\\"spike\\\",\\n \\\"dense\\\",\\n \\\"sparse\\\",\\n \\\"multivector\\\",\\n \\\"colbert\\\",\\n \\\"late\\\",\\n \\\"interaction\\\",\\n \\\"reranking\\\",\\n \\\"plain\\\",\\n \\\"summary\\\",\\n \\\"architecture_frontier_handoff\\\",\\n \\\"highest\\\",\\n\",\"exit\":0,\"description\":\"Check issue-162 research stage state\",\"truncated\":false},\"title\":\"Check issue-162 research stage state\",\"time\":{\"start\":1788986553977,\"end\":1788986553980}},\"id\":\"prt_g001a087e8704f0010b38Ld1TT\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788986553984,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_545636ccd237428faa036cde\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"curl -s \\\"http://localhost:3000/api/v1/repos/nick/rune/issues/162\\\" 2>/dev/null | head -c 1500; echo; curl -s \\\"http://localhost:3000/api/v1/repos/\\\" -o /dev/null -w \\\"%{http_code}\\\\n\\\" 2>/dev/null\",\"description\":\"Try Gitea API for issue 162\"},\"output\":\"\\n000\\n\",\"metadata\":{\"output\":\"\\n000\\n\",\"exit\":7,\"description\":\"Try Gitea API for issue 162\",\"truncated\":false},\"title\":\"Try Gitea API for issue 162\",\"time\":{\"start\":1788986553981,\"end\":1788986553983}},\"id\":\"prt_g001a087e8705d001tBRa6cwX4\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788986554156,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"id\":\"prt_g001a087e87680001tREL1xRpJ\",\"reason\":\"tool-calls\",\"snapshot\":\"b0498d5287e0e03c130a59d97d301726d5d535f9\",\"messageID\":\"msg_g001a087e838ae001WWkZOyLBR\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"type\":\"step-finish\",\"tokens\":{\"total\":38682,\"input\":2873,\"output\":673,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":35136}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788986560087,\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"part\":{\"id\":\"prt_g001a087e88e55001IYqYQelsU\",\"messageID\":\"msg_g001a087e877ee0016PAppIuDz\",\"sessionID\":\"ses_-ffe5f78183255ffeT8vvlzqHA\",\"snapshot\":\"23b9675157063d87bb8d458407705b5e28b4fe85\",\"type\":\"step-start\"}}\n19 /mnt/workdrive/issue161-harness/research/issue-162/stage_OUTWARD.events.jsonl\n","exit":0,"description":"Check event log for prior OUTWARD attempts","truncated":false},"title":"Check event log for prior OUTWARD attempts","time":{"start":1788986561855,"end":1788986561857}},"id":"prt_g001a087e88e560016N0SR1QRb","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e877ee0016PAppIuDz"}} +{"type":"step_finish","timestamp":1788986561884,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e89543001CEtsnJUqp","reason":"tool-calls","snapshot":"b71b6c26b9115e48b3dc3b254cba7fe9800e1e21","messageID":"msg_g001a087e877ee0016PAppIuDz","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":38811,"input":670,"output":125,"reasoning":0,"cache":{"write":0,"read":38016}},"cost":0}} +{"type":"step_start","timestamp":1788986564631,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8a015001xV9D8RQWD","messageID":"msg_g001a087e8957e001Z2TdmLjyI","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"a63b77adab8416bd5ce0d027469d80412d541745","type":"step-start"}} +{"type":"tool_use","timestamp":1788986566175,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_2330be2d498e4a75a1983fc4","state":{"status":"completed","input":{"command":"grep -c '\"type\":\"tool_use\"' /mnt/workdrive/issue161-harness/research/issue-162/stage_OUTWARD.events.jsonl; grep '\"tool\":\"webfetch\"\\|\"tool\":\"firecrawl' /mnt/workdrive/issue161-harness/research/issue-162/stage_OUTWARD.events.jsonl | head -3","description":"Check whether events log shows prior external calls"},"output":"9\n","metadata":{"output":"9\n","exit":0,"description":"Check whether events log shows prior external calls","truncated":false},"title":"Check whether events log shows prior external calls","time":{"start":1788986566170,"end":1788986566173}},"id":"prt_g001a087e8a016001FFNkhgYmR","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e8957e001Z2TdmLjyI"}} +{"type":"step_finish","timestamp":1788986566198,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8a61e001kKqRVaTAL","reason":"tool-calls","snapshot":"c97df61e52a6e819dfa46337f58e14c072b3956c","messageID":"msg_g001a087e8957e001Z2TdmLjyI","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":42112,"input":3298,"output":94,"reasoning":0,"cache":{"write":0,"read":38720}},"cost":0}} +{"type":"step_start","timestamp":1788986574759,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8c7a5001tHqZMpYIC","messageID":"msg_g001a087e8a656001Jne4IAT0n","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"2f8d18331bc031e384acd827f7551a16f1e4b27e","type":"step-start"}} +{"type":"text","timestamp":1788986577665,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8c947001w2EDz0FKU","messageID":"msg_g001a087e8a656001Jne4IAT0n","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"The events log only contains this session's activity — no prior external calls exist. I'm the OUTWARD executor; executing discovery now. Plan: (1) repo grounding receipts from the harness repo, (2) batch DuckDuckGo SERP discovery (established technique from prior rounds), (3) classify O-IDs, (4) read a bounded set, (5) extract evidence claims, (6) write `research/issue-162/outward.json`.\n\nFirst: repo-side grounding + first SERP batch in parallel.","time":{"start":1788986575175,"end":1788986577663}}} +{"type":"tool_use","timestamp":1788986577673,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_35686e69996741f1a0eafcff","state":{"status":"completed","input":{"command":"grep -n \"sqlite-vec\\|sqlite_vec\\|fts5\\|FTS5\" /mnt/workdrive/issue161-harness/src/rune/memory_store.py | head -10; echo ---; grep -rn \"qdrant\\|Qdrant\" /mnt/workdrive/issue161-harness/src/rune/ --include=\"*.py\" -i | head -5; echo \"---deps---\"; grep -i \"qdrant\\|sqlite-vec\" /mnt/workdrive/issue161-harness/pyproject.toml 2>/dev/null","description":"Repo grounding: memory_store vector tech + confirm no Qdrant dep"},"output":"5:file running sqlite-vec (dense vectors) + FTS5 (BM25) side by side.\n10: - HYBRID RETRIEVAL: dense (vec0) + BM25 (FTS5) fused by RRF (rank-based,\n26:Run under ~/.venvs/memory/bin/python3 (has sqlite-vec).\n122: import sqlite_vec\n124: sqlite_vec.load(db)\n129: CREATE VIRTUAL TABLE IF NOT EXISTS entries_fts USING fts5(\n149: CREATE VIRTUAL TABLE IF NOT EXISTS chunks_fts USING fts5(\n677: # (2) BM25 via FTS5.\n679: # SQLite FTS5 implicit AND): `MATCH \"black coffee\"` requires BOTH terms, so\n856: # Multiple conjunctive BM25 matches: the FTS5 implicit-AND already\n---\n---deps---\nmemory = [\"sqlite-vec>=0.1.0\"]\n","metadata":{"output":"5:file running sqlite-vec (dense vectors) + FTS5 (BM25) side by side.\n10: - HYBRID RETRIEVAL: dense (vec0) + BM25 (FTS5) fused by RRF (rank-based,\n26:Run under ~/.venvs/memory/bin/python3 (has sqlite-vec).\n122: import sqlite_vec\n124: sqlite_vec.load(db)\n129: CREATE VIRTUAL TABLE IF NOT EXISTS entries_fts USING fts5(\n149: CREATE VIRTUAL TABLE IF NOT EXISTS chunks_fts USING fts5(\n677: # (2) BM25 via FTS5.\n679: # SQLite FTS5 implicit AND): `MATCH \"black coffee\"` requires BOTH terms, so\n856: # Multiple conjunctive BM25 matches: the FTS5 implicit-AND already\n---\n---deps---\nmemory = [\"sqlite-vec>=0.1.0\"]\n","exit":0,"description":"Repo grounding: memory_store vector tech + confirm no Qdrant dep","truncated":false},"title":"Repo grounding: memory_store vector tech + confirm no Qdrant dep","time":{"start":1788986577670,"end":1788986577672}},"id":"prt_g001a087e8c959001RLlSmJLgt","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e8a656001Jne4IAT0n"}} +{"type":"tool_use","timestamp":1788986578518,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_a635cb595f534c76a00ddfa7","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=qdrant+colbert+late+interaction+reranking"},"output":"qdrant colbert late interaction reranking at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n Working with ColBERT - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.tech/documentation/fastembed/fastembed-colbert/\n \n \n \n \n \n\n \n \n Use FastEmbed to generate ColBERT multivectors and index them in Qdrant for late-interaction retrieval and reranking with native multivector support.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Multivectors and Late Interaction - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/\n \n \n \n \n \n\n \n \n Tutorial: use Qdrant multivectors with late interaction reranking to capture token-level signals and improve precision while controlling RAM and indexing cost.\n \n \n\n \n \n \n \n \n \n \n \n \n \n ColBERT and Late Interaction | qdrant/fastembed | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/qdrant/fastembed/7.3-colbert-and-late-interaction\n \n \n     2026-06-01T00:00:00.0000000\n \n \n \n \n\n \n \n This page explains how to use FastEmbed's implementation of ColBERT for late interaction embeddings. Late interaction is a retrieval approach that defers token-to-token interaction between queries and documents until the final ranking stage, offering improved precision compared to traditional dense embeddings. What is ColBERT and Late Interaction?\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - duksaramio/qdrant-hybrid-search-with-reranking-tutorial\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/duksaramio/qdrant-hybrid-search-with-reranking-tutorial\n \n \n     2026-08-16T17:02:00.0000000\n \n \n \n \n\n \n \n A production-grade, enterprise-ready implementation and comprehensive tutorial of Hybrid Search with Late Interaction (ColBERT MaxSim) Rerankingusing Qdrant, paired with an Autonomous GxP Quality Copilot built with Pydantic AI and local Ollama (muse-glimmer), and Local Langfuse Observability for 21 CFR Part 11 compliant audit trails, tailored specifically for Quality Assurance (QA), Quality ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n utility-tool-project/docs/qdrant-docs/fastembed_colbert_qdrant ... - GitHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/rdb420/utility-tool-project/blob/main/docs/qdrant-docs/fastembed_colbert_qdrant_reranking.md\n \n \n \n \n \n\n \n \n Matching is especially effective in Late Interaction models like ColBERT, which retain token-level embeddings and perform interaction during query time leading to relevance scoring. As you will see later in the tutorial, Qdrant supports multivectors and thus late interaction models natively.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Late Interaction in Qdrant - frutik.github.io\n \n\n \n\n \n \n \n \n \n \n \n \n \n frutik.github.io/awesome-search/Topics/Late-Interaction-in-Qdrant\n \n \n \n \n \n\n \n \n Late Interaction in Qdrant How Late Interaction models — ColBERT for text, ColPali for document images — are stored, scored, and reranked inside Qdrant Vector DB.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Late Interaction Models | qdrant/fastembed | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/qdrant/fastembed/5.3-late-interaction-models\n \n \n     2026-06-01T00:00:00.0000000\n \n \n \n \n\n \n \n Overview of Late Interaction Models Late interaction models, like ColBERT, create separate embeddings for each token in a document or query. This approach allows the model to perform "late interaction" — comparing query and document token embeddings at retrieval time rather than comparing single document/query vectors.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Late Interaction Text Embedding Models\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.github.io/fastembed/examples/ColBERT_with_FastEmbed/\n \n \n \n \n \n\n \n \n Late Interaction Text Embedding Models As of version 0.3.0 FastEmbed supports Late Interaction Text Embedding Models and currently available with one of the most popular embedding model of the family - ColBERT. What is a Late Interaction Text Embedding Model?\n \n \n\n \n \n \n \n \n \n \n \n \n \n multivector_representation_qdrant.ipynb - Colab\n \n\n \n\n \n \n \n \n \n \n \n \n \n colab.research.google.com/github/qdrant/examples/blob/master/multivector-representation/multivector_representation_qdrant.ipynb\n \n \n \n \n \n\n \n \n This example demonstrates how to use Qdrant's multi-vector search capabilities with both dense and late-interaction (ColBERT-style) embeddings for retrieval and reranking.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Reranking in Hybrid Search - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrantdb.de/documentation/advanced-tutorials/reranking-hybrid-search/\n \n \n \n \n \n\n \n \n By adding reranking with ColBERT, you can further refine search outputs for maximum relevance. In this guide, we'll show you how to implement hybrid search with reranking in Qdrant, leveraging dense, sparse, and late interaction embeddings to create an efficient, high-accuracy search system. Let's get started!\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=qdrant+colbert+late+interaction+reranking (text/html; charset=UTF-8)","time":{"start":1788986577666,"end":1788986578517}},"id":"prt_g001a087e8d108001EKkZBM7Z1","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e8a656001Jne4IAT0n"}} +{"type":"tool_use","timestamp":1788986578550,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_e02fe428967449bfa96d5961","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=qdrant+dense+sparse+multivector+vectors+hybrid+search+documentation"},"output":"qdrant dense sparse multivector vectors hybrid search documentation at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n Hybrid Queries - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.tech/documentation/search/hybrid-queries/\n \n \n \n \n \n\n \n \n Run hybrid queries in Qdrant: fuse dense, sparse, and multivector results with RRF or DBSF, layer custom scoring with Formula Query, and pick the right method for your data.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Hybrid Search - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.tech/documentation/search/text-search/hybrid-search/\n \n \n \n \n \n\n \n \n Build hybrid search pipelines in Qdrant by combining dense semantic vectors and sparse lexical vectors with the prefetch API and Reciprocal Rank Fusion for comprehensive search results.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Hybrid Search | qdrant/qdrant-client | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/qdrant/qdrant-client/5.4-hybrid-search\n \n \n     2026-04-13T00:00:00.0000000\n \n \n \n \n\n \n \n Hybrid Search Relevant source files Purpose and Scope This document explains the hybrid search functionality in the qdrant-client library. Hybrid search combines multiple search strategies—typically dense vectors (semantic) and sparse vectors (lexical)—to achieve superior retrieval accuracy. The client provides built-in fusion methods including Reciprocal Rank Fusion (RRF) and Distribution ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n search.qdrant.tech\n \n\n \n\n \n \n \n \n \n \n \n \n \n search.qdrant.tech/md/documentation/search/hybrid-queries\n \n \n \n \n \n\n \n \n For example, in text search, it is often useful to combine dense and sparse vectors to get the best of both worlds: semantic understanding from dense vectors and precise word matching from sparse vectors. Qdrant has a few ways of fusing the results from different queries: `rrf` and `dbsf` ### Reciprocal Rank Fusion (RRF) RRF considers the positions of results within each query and boosts those ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Hybrid Search with Qdrant - GitHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/Gbrlmoraes/hybrid_search_with_qdrant\n \n \n \n \n \n\n \n \n This repository provides a practical implementation of an advanced hybrid search system using the Qdrant vector database. It demonstrates how to combine traditional keyword-based search (sparse vectors) with modern semantic search (dense vectors) for highly relevant results. A reranking step further refines the output, creating a powerful and intelligent search experience.\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - qdrant/qdrant: Qdrant - High-performance, massive-scale Vector ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/qdrant/qdrant\n \n \n \n \n \n\n \n \n Qdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models like ColBERT.\n \n \n\n \n \n \n \n \n \n \n \n \n \n How to Build Hybrid Search in Qdrant (Dense + Sparse)\n \n\n \n\n \n \n \n \n \n \n \n \n \n milos-mitrovic.online/blog/how-to-build-hybrid-search-qdrant-dense-sparse-vectors\n \n \n \n \n \n\n \n \n Build hybrid search in Qdrant by fusing dense vectors and BM25 sparse vectors with the Query API and RRF. Step-by-step Python code, config, and fixes.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Hybrid Queries - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n frutik.github.io/awesome-search/Articles/Hybrid-Queries---Qdrant\n \n \n     2026-08-31T00:00:00.0000000\n \n \n \n \n\n \n \n Guidance for setting them: with an eval set, split queries in two, tune weights on the first half, measure on the second (a tune_rrf_weights grid-search helper is provided in Qdrant's "Choosing a Fusion Method" notebook); without an eval set, leave weights at the default (1.0, 1.0) — hand-tuned weights without measurement are unlikely ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Implementing Dynamic Chunking and Sparse Vector Search in Qdrant for ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n mohashari.github.io/implementing-dynamic-chunking-sparse-vector-search-qdrant-multi-tenant-rag/\n \n \n     2026-07-12T00:00:00.0000000\n \n \n \n \n\n \n \n Implementing Dynamic Chunking and Sparse Vector Search in Qdrant for Multi-Tenant RAG A production-focused guide to implementing dynamic, syntax-aware chunking and hybrid dense-sparse vector search in Qdrant with hard tenant isolation.\n \n \n\n \n \n \n \n \n \n \n \n \n \n How to perform hybrid search using Qdrant and sparse vectors for better ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.icertglobal.com/community/implementing-hybrid-search-with-qdrant-sparse-vectors\n \n \n \n \n \n\n \n \n Discover how to combine dense and sparse vectors in Qdrant to achieve high-precision hybrid search results for technical and SKU-based queries.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=qdrant+dense+sparse+multivector+vectors+hybrid+search+documentation (text/html; charset=UTF-8)","time":{"start":1788986577665,"end":1788986578548}},"id":"prt_g001a087e8cecd001aU5LdiPhC","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e8a656001Jne4IAT0n"}} +{"type":"step_finish","timestamp":1788986578576,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8d675001sAogf3R5T","reason":"tool-calls","snapshot":"890bd18329d5f81f720f1a453bca7351fd1397a8","messageID":"msg_g001a087e8a656001Jne4IAT0n","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":42713,"input":69,"output":596,"reasoning":0,"cache":{"write":0,"read":42048}},"cost":0}} +{"type":"step_start","timestamp":1788986588148,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8fbf200162bNIjBBY","messageID":"msg_g001a087e8d6b40015LkEMevAB","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"f0d91d9f5cd8d9fd2b3b97436b1e22595e6850de","type":"step-start"}} +{"type":"text","timestamp":1788986589294,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e8fcc0002T96k3IW7O","messageID":"msg_g001a087e8d6b40015LkEMevAB","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"Both SERPs returned strong results. Two more discovery queries for coverage of ops footprint and cost/trade-offs (relevant to the spike's feasibility angle).","time":{"start":1788986588352,"end":1788986589292}}} +{"type":"tool_use","timestamp":1788986590089,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_b6d3fa47d9744f2db3e635e3","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=qdrant+docker+local+deployment+memory+usage+resource+requirements+small+scale"},"output":"qdrant docker local deployment memory usage resource requirements small scale at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n Installation - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.tech/documentation/installation/\n \n \n \n \n \n\n \n \n Install Qdrant via Docker, Kubernetes, or binary releases — review CPU, memory, storage, and networking requirements for self-hosted deployments.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Fix Qdrant Docker OOMKilled: Set Memory Limits for Persistent Storage ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n markaicode.com/errors/qdrant-docker-deployment-failed-fix/\n \n \n     2026-06-24T00:00:00.0000000\n \n \n \n \n\n \n \n Qdrant container exits with code 137 and OOMKilled? Fix it in under 5 commands by setting --memory and --memory-swap Docker flags. Includes Error→Fix table, test environment, Docker Compose config, and 5 high-value FAQs for production deployments.\n \n \n\n \n \n \n \n \n \n \n \n \n \n RAM usage setup · qdrant · Discussion #168 · GitHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/orgs/qdrant/discussions/168\n \n \n \n \n \n\n \n \n Hi @arita37! Thanks for your interest in the project! How to limit Maximum RAM usage for Qdrant ? There is, currently, no direct configuration for this in the service. You can try to limit RAM usage using docker. Use --memory="1024mb" flag to limit total allowed memory per container. This method, however, won't prevent out-of-memory errors. It will only make them happen earlier. To make the ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Database Optimization - Qdrant\n \n\n \n\n \n \n \n \n \n \n \n \n \n qdrant.tech/documentation/faq/database-optimization/\n \n \n \n \n \n\n \n \n Configure Quantization to reduce the memory usage of vectors. Configure on-disk vector storage The choice of the approach depends on your requirements. Read more about configuring the optimal use of Qdrant. How do you choose the machine configuration? There are two main scenarios of Qdrant usage in terms of resource consumption:\n \n \n\n \n \n \n \n \n \n \n \n \n \n Qdrant in Production 2026: Setup & Quantization - Vucense\n \n\n \n\n \n \n \n \n \n \n \n \n \n vucense.com/dev-corner/qdrant-production-setup-2026/\n \n \n     2026-07-25T00:00:00.0000000\n \n \n \n \n\n \n \n Deploy with Docker Compose and a persistent volume mounted at /qdrant/storage. Ports: 6333 for the REST API, 6334 for gRPC — use gRPC for high-throughput ingestion. Memory: enable scalar (int8) quantization for roughly 4× RAM reduction at a small recall cost.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Docker Deployment | qdrant/qdrant | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/qdrant/qdrant/10.2-docker-deployment\n \n \n     2026-08-19T00:00:00.0000000\n \n \n \n \n\n \n \n This document describes Qdrant's Docker containerization strategy, including the multi-stage build process, image variants, build arguments, and CI/CD workflows for building and publishing container images. For information about configuration options within a running container, see Configuration System. For details about the overall build and CI/CD infrastructure, see Building and CI/CD.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Docker Deployments | qdrant/vector-db-benchmark | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/qdrant/vector-db-benchmark/5.1-docker-deployments\n \n \n     2025-09-08T00:00:00.0000000\n \n \n \n \n\n \n \n This document covers the Docker deployment configurations for all supported vector databases in the benchmarking system. Each vector database has multiple deployment variants optimized for different testing scenarios, including single-node, clustered, resource-limited, and continuous benchmarking configurations.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Qdrant Deployment Guide: Setup and Configuration (2026)\n \n\n \n\n \n \n \n \n \n \n \n \n \n adhdecode.com/articles/vector-databases/vector-database-qdrant-deployment-guide/\n \n \n     2026-04-18T00:00:00.0000000\n \n \n \n \n\n \n \n Qdrant can store up to 100x more vectors in RAM than you might expect, given its memory usage. Let's get Qdrant up and running. We'll start with a basic Docker Compose setup and then explore some key.\n \n \n\n \n \n \n \n \n \n \n \n \n \n skills/skills/qdrant-performance-optimization/memory-usage ... - GitHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/qdrant/skills/blob/main/skills/qdrant-performance-optimization/memory-usage-optimization/SKILL.md\n \n \n \n \n \n\n \n \n Optimal memory usage depends on the use case. For regular search scenarios, general guidelines are provided in the Capacity planning docs. For a detailed breakdown of memory usage at large scale, see Large scale memory usage example. Payload indexes and HNSW graph also require memory, along with vectors themselves, so it's important to consider them in calculations. Additionally, Qdrant ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n qdrant/qdrant - Docker Image\n \n\n \n\n \n \n \n \n \n \n \n \n \n hub.docker.com/r/qdrant/qdrant\n \n \n \n \n \n\n \n \n Qdrant(read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=qdrant+docker+local+deployment+memory+usage+resource+requirements+small+scale (text/html; charset=UTF-8)","time":{"start":1788986589293,"end":1788986590088}},"id":"prt_g001a087e8fcc50018CIltYjYm","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e8d6b40015LkEMevAB"}} +{"type":"tool_use","timestamp":1788986590389,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_8ab379a3144e453382844d11","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=colbert+late+interaction+reranking+cost+latency+tradeoffs+benchmark"},"output":"colbert late interaction reranking cost latency tradeoffs benchmark at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n ColBERT Late Interaction in ML Systems — Complete Guide…\n \n\n \n\n \n \n \n \n \n \n \n \n \n 123ofai.com/articles/blocks/colbert-late-interaction\n \n \n \n \n \n\n \n \n Technical guide to ColBERT late interaction retrieval for ML systems. Covers MaxSim scoring, ColBERTv2 compression, PLAID engine, and production RAG…\n \n \n\n \n \n \n \n \n \n \n \n \n \n Reranking: Cross-Encoders, ColBERT, Cohere | Neel Mishra\n \n\n \n\n \n \n \n \n \n \n \n \n \n neelmishra.github.io/blog/mlops/rag/reranking.html\n \n \n \n \n \n\n \n \n Reranking strategies for RAG: cross-encoders, ColBERT late interaction, Cohere Rerank API, latency tradeoffs, and production implementation.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Cross-Encoders, ColBERT, and LLM-Based Re-Rankers: A Practical ... - Medium\n \n\n \n\n \n \n \n \n \n \n \n \n \n medium.com/@aimichael/cross-encoders-colbert-and-llm-based-re-rankers-a-practical-guide-a23570d88548\n \n \n     2025-01-10T00:00:00.0000000\n \n \n \n \n\n \n \n Master Cross-Encoders, ColBERT, and LLM Re-Rankers to refine search results, boost relevance, and build efficient, scalable retrieval pipelines.\n \n \n\n \n \n \n \n \n \n \n \n \n \n RAG Reranking Explained: ColBERT, Cross-Encoders, and the LLM Rerank ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n thread-transfer.com/blog/2026-06-17-rag-reranking-llm-colbert/\n \n \n     2026-06-11T00:00:00.0000000\n \n \n \n \n\n \n \n Reranking RAG results with cross-encoders, ColBERT, and LLM rerankers. When to rerank, latency tradeoffs, and accuracy lifts measured.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Late-interaction reranking — ColBERT & beyond\n \n\n \n\n \n \n \n \n \n \n \n \n \n reranker.uk/guides/late-interaction-rerank.html\n \n \n     2026-06-25T00:00:00.0000000\n \n \n \n \n\n \n \n Latency budget is sub-50 ms. You cannot afford another transformer pass after retrieval; invest in better embeddings or late-interaction instead. Candidate count is huge per query. Reranking 500+ passages with a cross-encoder is slow; use late-interaction to prune to 50 first. Instruction-shaped queries.\n \n \n\n \n \n \n \n \n \n \n \n \n \n An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n weaviate.io/blog/late-interaction-overview\n \n \n     2025-04-09T00:00:00.0000000\n \n \n \n \n\n \n \n Late interaction allow for semantically rich interactions that enable a precise retrieval process across different modalities of unstructured data, including text and images.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Cross-Encoders, ColBERT, and LLM-Based Re-Rankers: A Practical Guide\n \n\n \n\n \n \n \n \n \n \n \n \n \n frutik.github.io/awesome-search/Articles/Cross-Encoders-ColBERT-and-LLM-Based-Re-Rankers\n \n \n \n \n \n\n \n \n Storage: can reach tens of gigabytesfor large catalogs Use when: large-scale reranking where cross-encoder latency is prohibitive LLM-Based Re-Rankers Apply flexible, dynamic criteria (freshness, authority, custom logic) without retraining Individual requests: "might cost a few cents and add over a second of latency"\n \n \n\n \n \n \n \n \n \n \n \n \n \n cross-encoder-and-reranking-demo/examples/08_colbert_late_interaction ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/ianhohoho/cross-encoder-and-reranking-demo/blob/main/examples/08_colbert_late_interaction.py\n \n \n \n \n \n\n \n \n Cross-encoder reranking techniques for information retrieval — from basic scoring to fine-tuning, caching, distillation, and ColBERT late interaction. Eight runnable examples with benchmarks.\n \n \n\n \n \n \n \n \n \n \n \n \n \n [2004.12832] ColBERT: Efficient and Effective Passage Search via ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n arxiv.org/abs/2004.12832\n \n \n \n \n \n\n \n \n Beyond reducing the cost of re-ranking the documents retrieved by a traditional model, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from a large document collection. We extensively evaluate ColBERT using two recent passage search datasets.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Reranking with ColBERT: Precision Without Pain - Medium\n \n\n \n\n \n \n \n \n \n \n \n \n \n medium.com/@ThinkingLoop/reranking-with-colbert-precision-without-pain-b390dda517f0\n \n \n     2025-09-26T00:00:00.0000000\n \n \n \n \n\n \n \n Reranking with ColBERT: Precision Without Pain A practical path to cross-encoder quality with bi-encoder speed — using late interaction and smart indexing.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=colbert+late+interaction+reranking+cost+latency+tradeoffs+benchmark (text/html; charset=UTF-8)","time":{"start":1788986589295,"end":1788986590387}},"id":"prt_g001a087e8fe93001Mhqpg5ZO1","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e8d6b40015LkEMevAB"}} +{"type":"step_finish","timestamp":1788986590413,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e904b5001dUYZia3zW","reason":"tool-calls","snapshot":"6f15b7dda5980c2646399c1d51c315034651494a","messageID":"msg_g001a087e8d6b40015LkEMevAB","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":46128,"input":3739,"output":213,"reasoning":0,"cache":{"write":0,"read":42176}},"cost":0}} +{"type":"step_start","timestamp":1788986596973,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e91e6b0015hu8A7mxM","messageID":"msg_g001a087e9051c001qEjp0noEp","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"5a53eeeabec9526768c087ed5a728664f4bf5a4e","type":"step-start"}} +{"type":"tool_use","timestamp":1788986597578,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_e42e1c0611194b42aa5c2857","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=sqlite-vec+vs+qdrant+embedded+vector+database+comparison"},"output":"DuckDuckGo\n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n \n \n \n DuckDuckGo\n \n \n\n \n \n \n \n \n Unfortunately, bots use DuckDuckGo too.\n Please complete the following challenge to confirm this search was made by a human.\n Select all squares containing a duck:\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Submit\n \n \n Images not loading?\n \n Please email the following code to:\n error-lite+aab3@duckduckgo.com\n Code: aab3","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=sqlite-vec+vs+qdrant+embedded+vector+database+comparison (text/html; charset=UTF-8)","time":{"start":1788986597425,"end":1788986597576}},"id":"prt_g001a087e91e6c001MzcFf17H0","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e9051c001qEjp0noEp"}} +{"type":"step_finish","timestamp":1788986597596,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e920c9001q200Laho8","reason":"tool-calls","snapshot":"7f678e4c8b3b5c3ac352488f7ad7385c18087948","messageID":"msg_g001a087e9051c001qEjp0noEp","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":49527,"input":3531,"output":44,"reasoning":0,"cache":{"write":0,"read":45952}},"cost":0}} +{"type":"step_start","timestamp":1788986617820,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e96fdb001rqOfVVHjX","messageID":"msg_g001a087e92102001NYiEiBq87","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"1d5d522f72a0bf7554f233daac9ec663392e73c2","type":"step-start"}} +{"type":"text","timestamp":1788986619492,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e973ed00107IFY3Ai4","messageID":"msg_g001a087e92102001NYiEiBq87","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"4 SERPs succeeded; the 5th hit a DuckDuckGo bot CAPTCHA (receipt preserved — recorded as limitation). Discovery is sufficient: ~20 candidate URLs across 4 query classes. Now classifying and reading a bounded selection — official docs (O1, O9, O16), primary paper (O12), independent vendor-neutral analysis (O11), and community ops ground truth (O17), plus the official repo README (O3) via raw.githubusercontent (established technique).","time":{"start":1788986618861,"end":1788986619489}}} +{"type":"tool_use","timestamp":1788986619586,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_479c213bee2e4463b2a3c341","state":{"status":"completed","input":{"format":"text","url":"https://arxiv.org/abs/2004.12832"},"output":"[2004.12832] ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n Skip to main content\n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n Search\n \n Submit\n Donate\n \n Log in\n \n\n\n\n \n \n Search arXiv\n \n \n \n \n \n Press Enter to search · Advanced search\n \n \n\n \n \n\n\n \n \n Computer Science > Information Retrieval\n \n\n \n arXiv:2004.12832 (cs)\n \n\n\n \n \n [Submitted on 27 Apr 2020 (v1), last revised 4 Jun 2020 (this version, v2)]\n Title:ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT\n Authors:Omar Khattab, Matei Zaharia View a PDF of the paper titled ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT, by Omar Khattab and Matei Zaharia\n View PDF\n HTML (experimental)\n\n\n\n \n Abstract:Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effective, the ranking models based on these LMs increase computational cost by orders of magnitude over prior approaches, particularly as they must feed each query-document pair through a massive neural network to compute a single relevance score. To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity. By delaying and yet retaining this fine-granular interaction, ColBERT can leverage the expressiveness of deep LMs while simultaneously gaining the ability to pre-compute document representations offline, considerably speeding up query processing. Beyond reducing the cost of re-ranking the documents retrieved by a traditional model, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from a large document collection. We extensively evaluate ColBERT using two recent passage search datasets. Results show that ColBERT's effectiveness is competitive with existing BERT-based models (and outperforms every non-BERT baseline), while executing two orders-of-magnitude faster and requiring four orders-of-magnitude fewer FLOPs per query.\n \n\n \n \n \n Comments:\n Accepted at SIGIR 2020\n \n\n Subjects:\n \n Information Retrieval (cs.IR); Computation and Language (cs.CL)\n \n Cite as:\n arXiv:2004.12832 [cs.IR]\n \n \n  \n (or \n arXiv:2004.12832v2 [cs.IR] for this version)\n \n \n \n  \n https://doi.org/10.48550/arXiv.2004.12832\n \n \n Focus to learn more\n \n \n \n arXiv-issued DOI via DataCite\n \n \n \n \n \n\n \n Submission history From: Omar Khattab [view email] [v1]\n Mon, 27 Apr 2020 14:21:03 UTC (1,437 KB)\n [v2]\n Thu, 4 Jun 2020 05:28:21 UTC (3,959 KB)\n\n \n \n \n \n Full-text links:\n Access Paper:\n \n \nView a PDF of the paper titled ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT, by Omar Khattab and Matei ZahariaView PDFHTML (experimental)TeX Source\n \n view license\n \n \n Additional Features\n \n Audio Summary\n \n \n \n Current browse context:\n cs.IR\n\n \n\n < prev\n \n   |   \n next >\n \n \n new\n | \n recent\n | 2020-04\n \n Change to browse by:\n \n cs\n cs.CL\n \n \n\n \n \n References & Citations\n \n NASA ADSGoogle Scholar\n Semantic Scholar\n \n \n \n\n \n \n 1 blog link (what is this?)\n \n \n\n DBLP - CS Bibliography\n \n listing | bibtex \n \nOmar KhattabMatei Zaharia \n \n export BibTeX citation\n Loading...\n\n\n\n \n \n BibTeX formatted citation\n ×\n \n \n loading...\n \n \n Data provided by: \n \n \n \n\n Bookmark\n \n \n \n \n \n \n \n\n\n \n Bibliographic Tools\n \n Bibliographic and Citation Tools\n \n \n \n \n \n \n Bibliographic Explorer Toggle\n \n \n \n Bibliographic Explorer (What is the Explorer?)\n \n \n \n \n \n \n \n Connected Papers Toggle\n \n \n \n Connected Papers (What is Connected Papers?)\n \n \n \n \n \n \n Litmaps Toggle\n \n \n \n Litmaps (What is Litmaps?)\n \n \n \n \n \n \n \n scite.ai Toggle\n \n \n \n scite Smart Citations (What are Smart Citations?)\n \n \n \n \n \n \n \n \n\n\n \n Code, Data, Media\n \n Code, Data and Media Associated with this Article\n \n \n \n \n \n \n alphaXiv Toggle\n \n \n \n alphaXiv (What is alphaXiv?)\n \n \n\n \n \n \n \n \n Links to Code Toggle\n \n \n \n CatalyzeX Code Finder for Papers (What is CatalyzeX?)\n \n \n\n \n \n \n \n \n DagsHub Toggle\n \n \n \n DagsHub (What is DagsHub?)\n \n \n \n \n \n \n \n \n GotitPub Toggle\n \n \n \n Gotit.pub (What is GotitPub?)\n \n \n\n \n \n \n \n \n Huggingface Toggle\n \n \n \n Hugging Face (What is Huggingface?)\n \n \n\n \n \n \n \n \n ScienceCast Toggle\n \n \n \n ScienceCast (What is ScienceCast?)\n \n \n \n\n \n \n \n \n \n \n \n\n\n \n Demos\n \n Demos\n \n \n \n \n \n \n Replicate Toggle\n \n \n \n Replicate (What is Replicate?)\n \n \n \n \n \n \n \n Spaces Toggle\n \n \n \n Hugging Face Spaces (What is Spaces?)\n \n \n \n \n \n \n \n Spaces Toggle\n \n \n \n TXYZ.AI (What is TXYZ.AI?)\n \n \n \n \n \n \n \n \n Related Papers\n \n Recommenders and Search Tools\n \n \n \n \n \n \n Link to Influence Flower\n \n \n \n Influence Flower (What are Influence Flowers?)\n \n \n \n \n \n \n \n Core recommender toggle\n \n \n \n CORE Recommender (What is CORE?)\n \n \n \n \n \n Author\n Venue\n Institution\n Topic\n \n \n \n \n \n \n \n \n \n \n \n\n \n \n About arXivLabs\n \n \n \n \n arXivLabs: experimental projects with community collaborators\n arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.\n \n \n \n \n \n \n\n \n\n\n \n Which authors of this paper are endorsers? |\n Disable MathJax (What is MathJax?)\n \n \n \n \n \n \n We gratefully acknowledge support from\n our major funders,\n member institutions, ,\n and all contributors.\n \n \n About\n ·\n Help\n ·\n Contact\n ·\n Subscribe\n ·\n Copyright\n ·\n Privacy\n ·\n Accessibility\n ·\n Operational Status (opens in new tab)\n \n \n\n \n Major funding support from","metadata":{"truncated":false},"title":"https://arxiv.org/abs/2004.12832 (text/html; charset=utf-8)","time":{"start":1788986619494,"end":1788986619585}},"id":"prt_g001a087e97411001W4CsunHH3","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e92102001NYiEiBq87"}} +{"type":"tool_use","timestamp":1788986619828,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_fac6be73fc90440aa7e68f48","state":{"status":"completed","input":{"format":"text","url":"https://qdrant.tech/documentation/search/hybrid-queries/"},"output":"Hybrid Queries - Qdrant\nDevelopDeployEcosystemLearnAPI Reference\nSearch\nLog in\nStart Free\n\nSearch\nDevelop\nDeploy\nEcosystem\nLearn\nAPI ReferenceGetting StartedOverviewWhat is Qdrant?Understanding Vector Search in QdrantLocal QuickstartCloud QuickstartUser ManualManage DataPointsVectorsPayloadCollectionsStorageIndexingQuantizationMultitenancyBulk UploadSearchSearchFilteringHybrid QueriesExploreSearch RelevanceLow-Latency SearchText SearchText FilteringFull-Text SearchHybrid SearchInferenceInference APIBM25Cloud InferenceExternal ProvidersMatryoshka ModelsQdrant EdgeEdge APIShard LifecycleConfigurationUpdating DataReading DataSnapshotsQuickstartOn-Device EmbeddingsBM25Data Synchronization PatternsSynchronize with a ServerQdrant Web UIAPI & SDKsQdrant ToolsAgent SkillsFastEmbedQuickstartFastEmbed & QdrantOptimize ThroughputWorking with miniCOILWorking with SPLADEWorking with ColBERTReranking with FastEmbedMulti-Vector PostprocessingQdrant MCP ServerTutorialsOverviewBasicsSemantic Search 101Multimodal SearchHybrid SearchHybrid Search with RerankingSearch EngineeringMultivectors and Late InteractionRelevance Feedback Retrieval in QdrantCollaborative FilteringMultivector Document RetrievalMeasuring ANN RecallMulti-Representation SearchBranch-Aware SearchIndexing Payloads of Random ShapeCompressed Multivector SearchStatic EmbeddingsOperations & ScaleSnapshotsData MigrationMigrate to a New Embedding ModelIncremental Embedding UpdatesPrevent Unoptimized UsageBlue-Green Cluster DeploymentTime-Based ShardingLarge-Scale SearchGPU-Accelerated HNSW IndexingSecure a Self-Hosted Qdrant InstanceDevelop & ImplementAsync APISemantic Search for CodeBuild a Semantic Search APIBuild a Hybrid Search APISupportRelease NotesFAQQdrant FundamentalsDatabase OptimizationGetting StartedOverviewWhat is Qdrant?Understanding Vector Search in QdrantLocal QuickstartCloud QuickstartUser ManualManage DataPointsVectorsPayloadCollectionsStorageIndexingQuantizationMultitenancyBulk UploadSearchSearchFilteringHybrid QueriesExploreSearch RelevanceLow-Latency SearchText SearchText FilteringFull-Text SearchHybrid SearchInferenceInference APIBM25Cloud InferenceExternal ProvidersMatryoshka ModelsQdrant EdgeEdge APIShard LifecycleConfigurationUpdating DataReading DataSnapshotsQuickstartOn-Device EmbeddingsBM25Data Synchronization PatternsSynchronize with a ServerQdrant Web UIAPI & SDKsQdrant ToolsAgent SkillsFastEmbedQuickstartFastEmbed & QdrantOptimize ThroughputWorking with miniCOILWorking with SPLADEWorking with ColBERTReranking with FastEmbedMulti-Vector PostprocessingQdrant MCP ServerTutorialsOverviewBasicsSemantic Search 101Multimodal SearchHybrid SearchHybrid Search with RerankingSearch EngineeringMultivectors and Late InteractionRelevance Feedback Retrieval in QdrantCollaborative FilteringMultivector Document RetrievalMeasuring ANN RecallMulti-Representation SearchBranch-Aware SearchIndexing Payloads of Random ShapeCompressed Multivector SearchStatic EmbeddingsOperations & ScaleSnapshotsData MigrationMigrate to a New Embedding ModelIncremental Embedding UpdatesPrevent Unoptimized UsageBlue-Green Cluster DeploymentTime-Based ShardingLarge-Scale SearchGPU-Accelerated HNSW IndexingSecure a Self-Hosted Qdrant InstanceDevelop & ImplementAsync APISemantic Search for CodeBuild a Semantic Search APIBuild a Hybrid Search APISupportRelease NotesFAQQdrant FundamentalsDatabase OptimizationDocumentationSearchHybrid QueriesHybrid and Multi-Stage QueriesAvailable as of v1.10.0With the introduction of multiple named vectors per point, there are use-cases when the best search is obtained by combining multiple queries,\nor by performing the search in more than one stage.Qdrant has a flexible and universal interface to make this possible, called Query API (API reference).The main component for making the combinations of queries possible is the prefetch parameter, which enables making sub-requests.Specifically, whenever a query has at least one prefetch, Qdrant will:Perform the prefetch query (or queries),Apply the main query over the results of its prefetch(es).Additionally, prefetches can have prefetches themselves, so you can have nested prefetches.Using offset parameter only affects the main query. This means that the prefetches must have a limit of at least limit + offset of the main query, otherwise you can get an empty result.Hybrid SearchOne of the most common problems when you have different representations of the same data is to combine the queried points for each representation into a single result.Fusing results from multiple queriesFor example, in text search, it is often useful to combine dense and sparse vectors to get the best of both worlds: semantic understanding from dense vectors and precise word matching from sparse vectors.Qdrant has a few ways of fusing the results from different queries: rrf and dbsfReciprocal Rank Fusion (RRF)RRF considers the positions of results within each query and boosts those that appear closer to the top in multiple sets of results. The score of a document is calculated using its rank in each result set:$$ score(d\\in D) = \\sum_{r_d\\in R(d)} \\frac{1}{k + \\frac{r_d + 1}{w_r} - 1} $$Where:$D$ the set of points across all results$R(d)$ is the set of rankings for a particular document$k$ is a constant (set to 2 by default)$r$ is an ordered set of results from one source$r_d$ is the rank of document $d$ in ranking $r$$w_r$ is the weight of ranking $r$ (set to 1 by default)Qdrant uses zero-based rank positions; the top result has $r_d = 0$.Because $w_r$ defaults to 1, without setting explicit weights, the formula can be simplified to the original RRF function:$$ score(d\\in D) = \\sum_{r_d\\in R(d)} \\frac{1}{k + r_d} $$Here is an example of RRF for a query containing two prefetches against different named vectors configured to hold sparse and dense vectors, respectively.POST /collections/{collection_name}/points/query\n{\n "prefetch": [\n {\n "query": { \n "indices": [1, 42], // <┐\n "values": [0.22, 0.8] // <┴─sparse vector\n },\n "using": "sparse",\n "limit": 20\n },\n {\n "query": [0.01, 0.45, 0.67, ...], // <-- dense vector\n "using": "dense",\n "limit": 20\n }\n ],\n "query": { "rrf": {} }, // <--- reciprocal rank fusion with defaults\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=[\n models.Prefetch(\n query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]),\n using="sparse",\n limit=20,\n ),\n models.Prefetch(\n query=[0.01, 0.45, 0.67], # <-- dense vector\n using="dense",\n limit=20,\n ),\n ],\n query=models.RrfQuery(rrf=models.Rrf()),\n limit=10,\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: [\n {\n query: {\n values: [0.22, 0.8],\n indices: [1, 42],\n },\n using: 'sparse',\n limit: 20,\n },\n {\n query: [0.01, 0.45, 0.67],\n using: 'dense',\n limit: 20,\n },\n ],\n query: {\n rrf: {},\n },\n limit: 10,\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder, RrfBuilder};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice()))\n .using("sparse")\n .limit(20u64)\n )\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n .using("dense")\n .limit(20u64)\n )\n .query(Query::new_rrf(RrfBuilder::default()))\n .limit(10u64)\n).await?;\nimport static io.qdrant.client.QueryFactory.nearest;\nimport static io.qdrant.client.QueryFactory.rrf;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\nimport io.qdrant.client.grpc.Points.Rrf;\nimport java.util.List;\n\nQdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient.queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n .addPrefetch(PrefetchQuery.newBuilder()\n .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42)))\n .setUsing("sparse")\n .setLimit(20)\n .build())\n .addPrefetch(PrefetchQuery.newBuilder()\n .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f)))\n .setUsing("dense")\n .setLimit(20)\n .build())\n .setQuery(rrf(Rrf.newBuilder().build()))\n .setLimit(10)\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List < PrefetchQuery > {\n new() {\n Query = new(float, uint)[] {\n (0.22f, 1), (0.8f, 42),\n },\n Using = "sparse",\n Limit = 20\n },\n new() {\n Query = new float[] {\n 0.01f, 0.45f, 0.67f\n },\n Using = "dense",\n Limit = 20\n }\n },\n query: new Rrf(),\n limit: 10\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t{\n\t\t\tQuery: qdrant.NewQuerySparse([]uint32{1, 42}, []float32{0.22, 0.8}),\n\t\t\tUsing: qdrant.PtrOf("sparse"),\n\t\t\tLimit: qdrant.PtrOf(uint64(20)),\n\t\t},\n\t\t{\n\t\t\tQuery: qdrant.NewQueryDense([]float32{0.01, 0.45, 0.67}),\n\t\t\tUsing: qdrant.PtrOf("dense"),\n\t\t\tLimit: qdrant.PtrOf(uint64(20)),\n\t\t},\n\t},\n\tQuery: qdrant.NewQueryRRF(&qdrant.Rrf{}),\n\tLimit: qdrant.PtrOf(uint64(10)),\n})\nSetting RRF Constant kAvailable as of v1.16.0To set the constant $k$ in the formula:POST /collections/{collection_name}/points/query\n{\n "prefetch": [\n // 2+ prefetches here\n ],\n "query": { "rrf": {"k": 60 } }, // <--- parameterized reciprocal rank fusion\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=[\n # 2+ prefetches here\n ],\n query=models.RrfQuery(rrf=models.Rrf(k=60)),\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: [\n // 2+ prefetches here\n ],\n query: { rrf: { k: 60 } },\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{RrfBuilder, Query, QueryPointsBuilder};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n // .add_prefetch(...) <┐\n // .add_prefetch(...) <┴─ 2+ prefetches here\n .query(Query::new_rrf(RrfBuilder::with_k(60)))\n).await?;\nimport static io.qdrant.client.QueryFactory.rrf;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\nimport io.qdrant.client.grpc.Points.Rrf;\nimport java.util.List;\n\nQdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient\n .queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n // .addPrefetch(...) <┐\n // .addPrefetch(...) <┴─ 2+ prefetches here\n .setQuery(rrf(Rrf.newBuilder().setK(60).build()))\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List<PrefetchQuery>\n {\n\t // 2+ prefetches here\n },\n query: new Rrf\n {\n\t K = 60,\n }\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t// 2+ prefetches here\n\t},\n\tQuery: qdrant.NewQueryRRF(\n\t\t&qdrant.Rrf{\n\t\t\tK: qdrant.PtrOf(uint32(60)),\n\t\t}),\n})\nWeighted RRFAvailable as of v1.17.0By default, each query is assigned an equal weight. In reality, one retriever is often stronger than the other for a given workload. For example, a dense retriever may dominate on natural-language queries, while BM25 may win on identifier-heavy ones. Assigning equal weight to both can let the weaker retriever drag down results. To address this, you can assign greater weight to rankers that perform well on your evaluation set.The rrf query allows you to configure relative weights for each of the prefetches. For example, if you have two prefetches and assign a weight of 3.0 to the first and 1.0 to the second, a document ranked third in the first query scores the same as a document ranked first in the second query. In the case of non-overlapping result sets, these weights return three results from the first set for every one result from the second set.Weights should be provided as an array of numbers, where each weight is applied to the corresponding prefetch in the order they are defined. The number of weights must match the number of prefetches.POST /collections/{collection_name}/points/query\n{\n "prefetch": [\n // Prefetches here\n ],\n "query": {\n "rrf": {\n "weights": [3.0, 1.0]\n }\n },\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=[\n # 2+ prefetches here\n ],\n query=models.RrfQuery(rrf=models.Rrf(weights=[3.0, 1.0])),\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: [\n // Prefetches here\n ],\n query: {\n rrf: {\n weights: [3.0, 1.0],\n },\n },\n limit: 10,\n});\nuse qdrant_client::qdrant::{Query, QueryPointsBuilder, RrfBuilder};\nuse qdrant_client::Qdrant;\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient\n .query(\n QueryPointsBuilder::new("{collection_name}")\n // .add_prefetch(...) <┐\n // .add_prefetch(...) <┴─ 2+ prefetches here\n .query(Query::new_rrf(RrfBuilder::new().weights(vec![3.0, 1.0]))),\n )\n .await?;\nimport static io.qdrant.client.QueryFactory.rrf;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\nimport io.qdrant.client.grpc.Points.Rrf;\nimport java.util.List;\n\nQdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient\n .queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n // .addPrefetch(...) <┐\n // .addPrefetch(...) <┴─ Prefetches here\n .setQuery(rrf(Rrf.newBuilder().addAllWeights(List.of(3.0f, 1.0f)).build()))\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List<PrefetchQuery>\n {\n\t // 2+ prefetches here\n },\n query: new Rrf\n {\n\t Weights = {3.0f, 1.0f},\n }\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t// Prefetches here\n\t},\n\tQuery: qdrant.NewQueryRRF(\n\t\t&qdrant.Rrf{\n\t\t\tWeights: []float32{3.0, 1.0},\n\t\t}),\n})\nWeights are a configuration choice, not something you can tune arbitrarily. The most reliable way to set them is by testing on your data.With an eval set (queries paired with known-relevant docs): split your eval queries in two. Try different weights on the first half, then measure on the second half. Measuring on the same queries you tuned on inflates the result. The Choosing a Fusion Method notebook provides a reusable tune_rrf_weights grid-search helper you can adapt to a train/val split.Without an eval set: leave weights at the default (1.0, 1.0). Hand-tuned weights without measurement are unlikely to beat the default reliably.Retune when your retrievers change (new embedding model, new chunking), when your corpus drifts substantially, or on a fixed cadence with a fresh eval sample.Distribution-Based Score Fusion (DBSF)Available as of v1.11.0DBSF keeps the raw scores from each query but normalizes their distributions before combining. For each retriever’s returned set, it computes the mean $\\mu$ and sample standard deviation $\\sigma$, then normalizes every score using the 3-sigma extremes as endpoints:$$ \\hat{s} = \\frac{s - (\\mu - 3\\sigma)}{6\\sigma} $$Normalized scores are summed across retrievers. Different score magnitudes no longer matter because each retriever contributes on the same comparable range.dbsf is stateless and computes its normalization limits from each query's returned points, not from all the scores it has seen. Scores are not clipped to [0, 1]; values outside the 3-sigma range remain outside it after the remap. If all returned scores are identical (or only one point is returned), DBSF emits 0.5 rather than dividing by zero.DBSF is a reasonable choice when you trust your retrievers’ raw scores to carry magnitude information. On well-calibrated retrievers DBSF can outperform tuned weighted RRF; on others weighted RRF wins. Neither dominates the other in general, so use your eval set to choose between them. Two caveats apply: the statistics come from the prefetch top-k (a small sample), and a single dominant outlier in that top-k can skew normalization for that query. Increase the prefetch limit if you see unstable rankings.POST /collections/{collection_name}/points/query\n{\n "prefetch": [\n {\n "query": { \n "indices": [1, 42], // <┐\n "values": [0.22, 0.8] // <┴─sparse vector\n },\n "using": "sparse",\n "limit": 20\n },\n {\n "query": [0.01, 0.45, 0.67, ...], // <-- dense vector\n "using": "dense",\n "limit": 20\n }\n ],\n "query": { "fusion": "dbsf" }, // <--- distribution-based score fusion\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=[\n models.Prefetch(\n query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]),\n using="sparse",\n limit=20,\n ),\n models.Prefetch(\n query=[0.01, 0.45, 0.67], # <-- dense vector\n using="dense",\n limit=20,\n ),\n ],\n query=models.FusionQuery(fusion=models.Fusion.DBSF),\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: [\n {\n query: {\n values: [0.22, 0.8],\n indices: [1, 42],\n },\n using: 'sparse',\n limit: 20,\n },\n {\n query: [0.01, 0.45, 0.67],\n using: 'dense',\n limit: 20,\n },\n ],\n query: {\n fusion: 'dbsf',\n },\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{Fusion, PrefetchQueryBuilder, Query, QueryPointsBuilder};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice()))\n .using("sparse")\n .limit(20u64)\n )\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n .using("dense")\n .limit(20u64)\n )\n .query(Query::new_fusion(Fusion::Dbsf))\n).await?;\nimport static io.qdrant.client.QueryFactory.fusion;\nimport static io.qdrant.client.QueryFactory.nearest;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.Fusion;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\nimport java.util.List;\n\nQdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient.queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n .addPrefetch(PrefetchQuery.newBuilder()\n .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42)))\n .setUsing("sparse")\n .setLimit(20)\n .build())\n .addPrefetch(PrefetchQuery.newBuilder()\n .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f)))\n .setUsing("dense")\n .setLimit(20)\n .build())\n .setQuery(fusion(Fusion.DBSF))\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List < PrefetchQuery > {\n new() {\n Query = new(float, uint)[] {\n (0.22f, 1), (0.8f, 42),\n },\n Using = "sparse",\n Limit = 20\n },\n new() {\n Query = new float[] {\n 0.01f, 0.45f, 0.67f\n },\n Using = "dense",\n Limit = 20\n }\n },\n query: Fusion.Dbsf\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t{\n\t\t\tQuery: qdrant.NewQuerySparse([]uint32{1, 42}, []float32{0.22, 0.8}),\n\t\t\tUsing: qdrant.PtrOf("sparse"),\n\t\t\tLimit: qdrant.PtrOf(uint64(20)),\n\t\t},\n\t\t{\n\t\t\tQuery: qdrant.NewQueryDense([]float32{0.01, 0.45, 0.67}),\n\t\t\tUsing: qdrant.PtrOf("dense"),\n\t\t\tLimit: qdrant.PtrOf(uint64(20)),\n\t\t},\n\t},\n\tQuery: qdrant.NewQueryFusion(qdrant.Fusion_DBSF),\n})\nChoosing a Fusion MethodIf you have…UseAn eval set (queries with known-relevant docs) to tune onWeighted RRF, with weights tuned on a train/val splitTrust in your retrievers’ raw scores and no eval setDBSFNeither an eval set nor strong score priorsRRF (the safe default)For a deeper breakdown of when to prefer each, see the FAQ on RRF vs. DBSF. To layer business logic (recency, popularity, geo) on top of a fused result, see Custom scoring with a formula query.A common request is \"alpha-weighted linear combination of dense and sparse scores.\" This is unreliable without first normalizing the scores: dense (cosine, bounded) and sparse (BM25, unbounded) scores live on different scales that also shift per query, so a fixed alpha over raw scores tends to be dominated by whichever retriever has larger raw magnitudes on a given query. RRF sidesteps this by using ranks. DBSF sidesteps it by normalizing distributions.Multi-Stage QueriesIn general, larger vector representations give more accurate search results, but makes them more expensive to compute.Splitting the search into two stages is a known technique to mitigate this effect:First, use a smaller and cheaper representation to get a large list of candidates.Then, re-score the candidates using the larger and more accurate representation.There are a few ways to build search architectures around this idea:The quantized vectors as a first stage, and the full-precision vectors as a second stage.Leverage Matryoshka Representation Learning (MRL) to generate candidate vectors with a shorter vector, and then refine them with a longer one.Use regular dense vectors to pre-fetch the candidates, and then re-score them with a multi-vector model like ColBERT.To get the best of all worlds, Qdrant has a convenient interface to perform the queries in stages,\nsuch that the coarse results are fetched first, and then they are refined later with larger vectors.Disable the HNSW index for vectors used only for rescoring by setting m=0 in the vector's HNSW configuration. Rescoring does not use the HNSW index, so disabling it will free up memory.Re-Scoring ExamplesFetch 1000 results using a shorter MRL byte vector, then re-score them using the full vector and get the top 10.POST /collections/{collection_name}/points/query\n{\n "prefetch": {\n "query": [1, 23, 45, 67], // <------------- small byte vector\n "using": "mrl_byte",\n "limit": 1000\n },\n "query": [0.01, 0.299, 0.45, 0.67, ...], // <-- full vector\n "using": "full",\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=models.Prefetch(\n query=[1, 23, 45, 67], # <------------- small byte vector\n using="mrl_byte",\n limit=1000,\n ),\n query=[0.01, 0.299, 0.45, 0.67], # <-- full vector\n using="full",\n limit=10,\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: {\n query: [1, 23, 45, 67], // <------------- small byte vector\n using: 'mrl_byte',\n limit: 1000,\n },\n query: [0.01, 0.299, 0.45, 0.67], // <-- full vector,\n using: 'full',\n limit: 10,\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest(vec![1.0, 23.0, 45.0, 67.0]))\n .using("mlr_byte")\n .limit(1000u64)\n )\n .query(Query::new_nearest(vec![0.01, 0.299, 0.45, 0.67]))\n .using("full")\n .limit(10u64)\n).await?;\nimport static io.qdrant.client.QueryFactory.nearest;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\n\nQdrantClient client =\n new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient\n .queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .setQuery(nearest(1, 23, 45, 67))\t// <------------- small byte vector\n .setLimit(1000)\n .setUsing("mrl_byte")\n .build())\n .setQuery(nearest(0.01f, 0.299f, 0.45f, 0.67f))\t // <-- full vector\n .setUsing("full")\n .setLimit(10)\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List<PrefetchQuery> {\n new() {\n Query = new float[] { 1,23, 45, 67 }, // <------------- small byte vector\n Using = "mrl_byte",\n Limit = 1000\n }\n },\n query: new float[] { 0.01f, 0.299f, 0.45f, 0.67f }, // <-- full vector\n usingVector: "full",\n limit: 10\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t{\n\t\t\tQuery: qdrant.NewQueryDense([]float32{1, 23, 45, 67}),\n\t\t\tUsing: qdrant.PtrOf("mrl_byte"),\n\t\t\tLimit: qdrant.PtrOf(uint64(1000)),\n\t\t},\n\t},\n\tQuery: qdrant.NewQueryDense([]float32{0.01, 0.299, 0.45, 0.67}),\n\tUsing: qdrant.PtrOf("full"),\n})\nFetch 100 results using the default vector, then re-score them using a multi-vector to get the top 10.POST /collections/{collection_name}/points/query\n{\n "prefetch": {\n "query": [0.01, 0.45, 0.67, ...], // <-- dense vector\n "limit": 100\n },\n "query": [ // <─┐\n [0.1, 0.2, ...], // < │\n [0.2, 0.1, ...], // < ├─ multi-vector\n [0.8, 0.9, ...] // < │\n ], // <─┘ \n "using": "colbert",\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=models.Prefetch(\n query=[0.01, 0.45, 0.67, 0.53], # <-- dense vector\n limit=100,\n ),\n query=[\n [0.1, 0.2, 0.32], # <─┐\n [0.2, 0.1, 0.52], # < ├─ multi-vector\n [0.8, 0.9, 0.93], # < ┘\n ],\n using="colbert",\n limit=10,\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: {\n query: [1, 23, 45, 67], // <------------- small byte vector\n limit: 100,\n },\n query: [\n [0.1, 0.2], // <─┐\n [0.2, 0.1], // < ├─ multi-vector\n [0.8, 0.9], // < ┘\n ],\n using: 'colbert',\n limit: 10,\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n .limit(100u64)\n )\n .query(Query::new_nearest(vec![\n vec![0.1, 0.2],\n vec![0.2, 0.1],\n vec![0.8, 0.9],\n ]))\n .using("colbert")\n .limit(10u64)\n).await?;\nimport static io.qdrant.client.QueryFactory.nearest;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\n\nQdrantClient client =\n new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient\n .queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .setQuery(nearest(0.01f, 0.45f, 0.67f)) // <-- dense vector\n .setLimit(100)\n .build())\n .setQuery(\n nearest(\n new float[][] {\n {0.1f, 0.2f},\t// <─┐\n {0.2f, 0.1f},\t// < ├─ multi-vector\n {0.8f, 0.9f}\t// < ┘\n }))\n .setUsing("colbert")\n .setLimit(10)\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List <PrefetchQuery> {\n new() {\n Query = new float[] { 0.01f, 0.45f, 0.67f\t},\t// <-- dense vector****\n Limit = 100\n }\n },\n query: new float[][] {\n [0.1f, 0.2f], // <─┐\n [0.2f, 0.1f], // < ├─ multi-vector\n [0.8f, 0.9f] // < ┘\n },\n usingVector: "colbert",\n limit: 10\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t{\n\t\t\tQuery: qdrant.NewQueryDense([]float32{0.01, 0.45, 0.67}),\n\t\t\tLimit: qdrant.PtrOf(uint64(100)),\n\t\t},\n\t},\n\tQuery: qdrant.NewQueryMulti([][]float32{\n\t\t{0.1, 0.2},\n\t\t{0.2, 0.1},\n\t\t{0.8, 0.9},\n\t}),\n\tUsing: qdrant.PtrOf("colbert"),\n})\nYou can combine all of these techniques in a single query:POST /collections/{collection_name}/points/query\n{\n "prefetch": {\n "prefetch": {\n "query": [1, 23, 45, 67], // <------ small byte vector\n "using": "mrl_byte",\n "limit": 1000\n },\n "query": [0.01, 0.45, 0.67, ...], // <-- full dense vector\n "using": "full",\n "limit": 100\n },\n "query": [ // <─┐\n [0.1, 0.2, ...], // < │\n [0.2, 0.1, ...], // < ├─ multi-vector\n [0.8, 0.9, ...] // < │\n ], // <─┘ \n "using": "colbert",\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=models.Prefetch(\n prefetch=models.Prefetch(\n query=[1, 23, 45, 67], # <------ small byte vector\n using="mrl_byte",\n limit=1000,\n ),\n query=[0.01, 0.45, 0.67], # <-- full dense vector\n using="full",\n limit=100,\n ),\n query=[\n [0.17, 0.23, 0.52], # <─┐\n [0.22, 0.11, 0.63], # < ├─ multi-vector\n [0.86, 0.93, 0.12], # < ┘\n ],\n using="colbert",\n limit=10,\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nclient.query("{collection_name}", {\n prefetch: {\n prefetch: {\n query: [1, 23, 45, 67], // <------------- small byte vector\n using: 'mrl_byte',\n limit: 1000,\n },\n query: [0.01, 0.45, 0.67], // <-- full dense vector\n using: 'full',\n limit: 100,\n },\n query: [\n [0.1, 0.2], // <─┐\n [0.2, 0.1], // < ├─ multi-vector\n [0.8, 0.9], // < ┘\n ],\n using: 'colbert',\n limit: 10,\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n .add_prefetch(PrefetchQueryBuilder::default()\n .add_prefetch(PrefetchQueryBuilder::default()\n .query(Query::new_nearest(vec![1.0, 23.0, 45.0, 67.0]))\n .using("mlr_byte")\n .limit(1000u64)\n )\n .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n .using("full")\n .limit(100u64)\n )\n .query(Query::new_nearest(vec![\n vec![0.1, 0.2],\n vec![0.2, 0.1],\n vec![0.8, 0.9],\n ]))\n .using("colbert")\n .limit(10u64)\n).await?;\nimport static io.qdrant.client.QueryFactory.nearest;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\n\nQdrantClient client =\n new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient\n .queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .setQuery(nearest(1, 23, 45, 67))\t// <------------- small byte vector\n .setUsing("mrl_byte")\n .setLimit(1000)\n .build())\n .setQuery(nearest(0.01f, 0.45f, 0.67f)) // <-- dense vector\n .setUsing("full")\n .setLimit(100)\n .build())\n .setQuery(\n nearest(\n new float[][] {\n {0.1f, 0.2f},\t// <─┐\n {0.2f, 0.1f},\t// < ├─ multi-vector\n {0.8f, 0.9f}\t// < ┘\n }))\n .setUsing("colbert")\n .setLimit(10)\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch: new List <PrefetchQuery> {\n new() {\n Prefetch = {\n new List <PrefetchQuery> {\n new() {\n Query = new float[] { 1, 23, 45, 67 }, // <------------- small byte vector\n Using = "mrl_byte",\n Limit = 1000\n },\n }\n },\n Query = new float[] {0.01f, 0.45f, 0.67f}, // <-- dense vector\n Using = "full",\n Limit = 100\n }\n },\n query: new float[][] {\n [0.1f, 0.2f], // <─┐\n [0.2f, 0.1f], // < ├─ multi-vector\n [0.8f, 0.9f] // < ┘\n },\n usingVector: "colbert",\n limit: 10\n);\nimport (\n\t"context"\n\n\t"github.com/qdrant/go-client/qdrant"\n)\n\nclient, err := qdrant.NewClient(&qdrant.Config{\n\tHost: "localhost",\n\tPort: 6334,\n})\n\nclient.Query(context.Background(), &qdrant.QueryPoints{\n\tCollectionName: "{collection_name}",\n\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t{\n\t\t\tPrefetch: []*qdrant.PrefetchQuery{\n\t\t\t\t{\n\t\t\t\t\tQuery: qdrant.NewQueryDense([]float32{1, 23, 45, 67}),\n\t\t\t\t\tUsing: qdrant.PtrOf("mrl_byte"),\n\t\t\t\t\tLimit: qdrant.PtrOf(uint64(1000)),\n\t\t\t\t},\n\t\t\t},\n\t\t\tQuery: qdrant.NewQueryDense([]float32{0.01, 0.45, 0.67}),\n\t\t\tLimit: qdrant.PtrOf(uint64(100)),\n\t\t\tUsing: qdrant.PtrOf("full"),\n\t\t},\n\t},\n\tQuery: qdrant.NewQueryMulti([][]float32{\n\t\t{0.1, 0.2},\n\t\t{0.2, 0.1},\n\t\t{0.8, 0.9},\n\t}),\n\tUsing: qdrant.PtrOf("colbert"),\n})\nCustom Scoring with a Formula QueryAvailable as of v1.14.0A formula query lets you compose a final score from prefetch scores ($score), payload fields, and built-in helpers like exponential or Gaussian decay. The typical pattern is to fuse retrievers with RRF or DBSF in a prefetch, then wrap that prefetch in a formula query that layers ranking logic on top: recency decay, popularity boosts, geo decay, or category-conditional multipliers.POST /collections/{collection_name}/points/query\n{\n "prefetch": {\n "prefetch": [\n {\n "query": {\n "indices": [1, 42], // <┐\n "values": [0.22, 0.8] // <┴─sparse vector\n },\n "using": "sparse",\n "limit": 100\n },\n {\n "query": [0.01, 0.45, 0.67, ...], // <-- dense vector\n "using": "dense",\n "limit": 100\n }\n ],\n "query": { "rrf": {} },\n "limit": 100\n },\n "query": {\n "formula": {\n "sum": [\n "$score", // the fused score from the RRF prefetch\n {\n "mult": [\n 0.1, // caps decay contribution\n {\n "exp_decay": {\n "x": {\n "datetime_key": "published_at"\n },\n "target": {\n "datetime": "YYYY-MM-DDT00:00:00Z"\n },\n "scale": 15552000, // 180 days in seconds\n "midpoint": 0.5\n }\n }\n ]\n }\n ]\n }\n },\n "limit": 10\n}\nfrom qdrant_client import QdrantClient, models\n\nclient = QdrantClient(url="http://localhost:6333")\n\nclient.query_points(\n collection_name="{collection_name}",\n prefetch=models.Prefetch(\n prefetch=[\n models.Prefetch(\n query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]),\n using="sparse",\n limit=100,\n ),\n models.Prefetch(\n query=[0.01, 0.45, 0.67], # <-- dense vector\n using="dense",\n limit=100,\n ),\n ],\n query=models.RrfQuery(rrf=models.Rrf()),\n limit=100,\n ),\n query=models.FormulaQuery(\n formula=models.SumExpression(\n sum=[\n "$score", # the fused score from the RRF prefetch\n models.MultExpression(mult=[\n 0.1, # caps decay contribution; un-weighted decay [0, 1] would otherwise crowd out small RRF scores\n models.ExpDecayExpression(\n exp_decay=models.DecayParamsExpression(\n x=models.DatetimeKeyExpression(datetime_key="published_at"),\n target=models.DatetimeExpression(datetime="YYYY-MM-DDT00:00:00Z"),\n scale=86400 * 180, # 180 days in seconds\n midpoint=0.5,\n )\n ),\n ]),\n ]\n )\n ),\n limit=10,\n)\nimport { QdrantClient } from "@qdrant/js-client-rest";\n\nconst client = new QdrantClient({ host: "localhost", port: 6333 });\n\nawait client.query("{collection_name}", {\n prefetch: {\n prefetch: [\n {\n query: {\n values: [0.22, 0.8],\n indices: [1, 42],\n },\n using: "sparse",\n limit: 100,\n },\n {\n query: [0.01, 0.45, 0.67], // <-- dense vector\n using: "dense",\n limit: 100,\n },\n ],\n query: { rrf: {} },\n limit: 100,\n },\n query: {\n formula: {\n sum: [\n "$score", // the fused score from the RRF prefetch\n {\n mult: [\n 0.1, // caps decay contribution; un-weighted decay [0, 1] would otherwise crowd out small RRF scores\n {\n exp_decay: {\n x: { datetime_key: "published_at" },\n target: { datetime: "YYYY-MM-DDT00:00:00Z" },\n scale: 86400 * 180, // 180 days in seconds\n midpoint: 0.5,\n },\n },\n ],\n },\n ],\n },\n },\n limit: 10,\n});\nuse qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{\n DecayParamsExpressionBuilder, Expression, FormulaBuilder, PrefetchQueryBuilder, Query,\n QueryPointsBuilder, RrfBuilder,\n};\n\nlet client = Qdrant::from_url("http://localhost:6334").build()?;\n\nclient.query(\n QueryPointsBuilder::new("{collection_name}")\n .add_prefetch(\n PrefetchQueryBuilder::default()\n .add_prefetch(\n PrefetchQueryBuilder::default()\n .query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice()))\n .using("sparse")\n .limit(100u64),\n )\n .add_prefetch(\n PrefetchQueryBuilder::default()\n .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n .using("dense")\n .limit(100u64),\n )\n .query(Query::new_rrf(RrfBuilder::default()))\n .limit(100u64),\n )\n .query(\n FormulaBuilder::new(Expression::sum_with([\n Expression::score(),\n Expression::mult_with([\n Expression::constant(0.1),\n Expression::exp_decay(\n DecayParamsExpressionBuilder::new(Expression::datetime_key("published_at"))\n .target(Expression::datetime("YYYY-MM-DDT00:00:00Z"))\n .scale(86400.0 * 180.0)\n .midpoint(0.5),\n ),\n ]),\n ])),\n )\n .limit(10u64),\n)\n.await?;\nimport static io.qdrant.client.ExpressionFactory.constant;\nimport static io.qdrant.client.ExpressionFactory.datetime;\nimport static io.qdrant.client.ExpressionFactory.datetimeKey;\nimport static io.qdrant.client.ExpressionFactory.expDecay;\nimport static io.qdrant.client.ExpressionFactory.mult;\nimport static io.qdrant.client.ExpressionFactory.sum;\nimport static io.qdrant.client.ExpressionFactory.variable;\nimport static io.qdrant.client.QueryFactory.formula;\nimport static io.qdrant.client.QueryFactory.nearest;\nimport static io.qdrant.client.QueryFactory.rrf;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.DecayParamsExpression;\nimport io.qdrant.client.grpc.Points.Formula;\nimport io.qdrant.client.grpc.Points.MultExpression;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\nimport io.qdrant.client.grpc.Points.Rrf;\nimport io.qdrant.client.grpc.Points.SumExpression;\nimport java.util.List;\n\nQdrantClient client =\n new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());\n\nclient.queryAsync(\n QueryPoints.newBuilder()\n .setCollectionName("{collection_name}")\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42)))\n .setUsing("sparse")\n .setLimit(100)\n .build())\n .addPrefetch(\n PrefetchQuery.newBuilder()\n .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f)))\n .setUsing("dense")\n .setLimit(100)\n .build())\n .setQuery(rrf(Rrf.newBuilder().build()))\n .setLimit(100)\n .build())\n .setQuery(\n formula(\n Formula.newBuilder()\n .setExpression(\n sum(\n SumExpression.newBuilder()\n .addSum(variable("$score"))\n .addSum(\n mult(\n MultExpression.newBuilder()\n .addMult(constant(0.1f))\n .addMult(\n expDecay(\n DecayParamsExpression.newBuilder()\n .setX(datetimeKey("published_at"))\n .setTarget(\n datetime("YYYY-MM-DDT00:00:00Z"))\n .setScale(86400 * 180)\n .setMidpoint(0.5f)\n .build()))\n .build()))\n .build()))\n .build()))\n .setLimit(10)\n .build())\n .get();\nusing Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient("localhost", 6334);\n\nawait client.QueryAsync(\n collectionName: "{collection_name}",\n prefetch:\n [\n new PrefetchQuery {\n Prefetch = {\n new PrefetchQuery {\n Query = new(float, uint)[] { (0.22f, 1), (0.8f, 42) },\n\n...7660 bytes truncated...\n\nThe tool call succeeded but the output was truncated. Full output saved to: /home/nick/.local/share/mimocode/tool-output/tool_g001a087e977ad001HEG21pT0W\nUse the actor tool to have explore agent process this file with Grep and Read (with offset/limit). Do NOT read the full file yourself - delegate to save context.","metadata":{"truncated":true,"outputPath":"/home/nick/.local/share/mimocode/tool-output/tool_g001a087e977ad001HEG21pT0W"},"title":"https://qdrant.tech/documentation/search/hybrid-queries/ (text/html; charset=UTF-8)","time":{"start":1788986619490,"end":1788986619825}},"id":"prt_g001a087e973fd001BWURMxUqL","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e92102001NYiEiBq87"}} +{"type":"tool_use","timestamp":1788986619831,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_f3be5019786847d18ac8d1db","state":{"status":"completed","input":{"format":"text","url":"https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/"},"output":"Multivectors and Late Interaction - Qdrant\nDevelopDeployEcosystemLearnAPI Reference\nSearch\nLog in\nStart Free\n\nSearch\nDevelop\nDeploy\nEcosystem\nLearn\nAPI ReferenceGetting StartedOverviewWhat is Qdrant?Understanding Vector Search in QdrantLocal QuickstartCloud QuickstartUser ManualManage DataPointsVectorsPayloadCollectionsStorageIndexingQuantizationMultitenancyBulk UploadSearchSearchFilteringHybrid QueriesExploreSearch RelevanceLow-Latency SearchText SearchText FilteringFull-Text SearchHybrid SearchInferenceInference APIBM25Cloud InferenceExternal ProvidersMatryoshka ModelsQdrant EdgeEdge APIShard LifecycleConfigurationUpdating DataReading DataSnapshotsQuickstartOn-Device EmbeddingsBM25Data Synchronization PatternsSynchronize with a ServerQdrant Web UIAPI & SDKsQdrant ToolsAgent SkillsFastEmbedQuickstartFastEmbed & QdrantOptimize ThroughputWorking with miniCOILWorking with SPLADEWorking with ColBERTReranking with FastEmbedMulti-Vector PostprocessingQdrant MCP ServerTutorialsOverviewBasicsSemantic Search 101Multimodal SearchHybrid SearchHybrid Search with RerankingSearch EngineeringMultivectors and Late InteractionRelevance Feedback Retrieval in QdrantCollaborative FilteringMultivector Document RetrievalMeasuring ANN RecallMulti-Representation SearchBranch-Aware SearchIndexing Payloads of Random ShapeCompressed Multivector SearchStatic EmbeddingsOperations & ScaleSnapshotsData MigrationMigrate to a New Embedding ModelIncremental Embedding UpdatesPrevent Unoptimized UsageBlue-Green Cluster DeploymentTime-Based ShardingLarge-Scale SearchGPU-Accelerated HNSW IndexingSecure a Self-Hosted Qdrant InstanceDevelop & ImplementAsync APISemantic Search for CodeBuild a Semantic Search APIBuild a Hybrid Search APISupportRelease NotesFAQQdrant FundamentalsDatabase OptimizationGetting StartedOverviewWhat is Qdrant?Understanding Vector Search in QdrantLocal QuickstartCloud QuickstartUser ManualManage DataPointsVectorsPayloadCollectionsStorageIndexingQuantizationMultitenancyBulk UploadSearchSearchFilteringHybrid QueriesExploreSearch RelevanceLow-Latency SearchText SearchText FilteringFull-Text SearchHybrid SearchInferenceInference APIBM25Cloud InferenceExternal ProvidersMatryoshka ModelsQdrant EdgeEdge APIShard LifecycleConfigurationUpdating DataReading DataSnapshotsQuickstartOn-Device EmbeddingsBM25Data Synchronization PatternsSynchronize with a ServerQdrant Web UIAPI & SDKsQdrant ToolsAgent SkillsFastEmbedQuickstartFastEmbed & QdrantOptimize ThroughputWorking with miniCOILWorking with SPLADEWorking with ColBERTReranking with FastEmbedMulti-Vector PostprocessingQdrant MCP ServerTutorialsOverviewBasicsSemantic Search 101Multimodal SearchHybrid SearchHybrid Search with RerankingSearch EngineeringMultivectors and Late InteractionRelevance Feedback Retrieval in QdrantCollaborative FilteringMultivector Document RetrievalMeasuring ANN RecallMulti-Representation SearchBranch-Aware SearchIndexing Payloads of Random ShapeCompressed Multivector SearchStatic EmbeddingsOperations & ScaleSnapshotsData MigrationMigrate to a New Embedding ModelIncremental Embedding UpdatesPrevent Unoptimized UsageBlue-Green Cluster DeploymentTime-Based ShardingLarge-Scale SearchGPU-Accelerated HNSW IndexingSecure a Self-Hosted Qdrant InstanceDevelop & ImplementAsync APISemantic Search for CodeBuild a Semantic Search APIBuild a Hybrid Search APISupportRelease NotesFAQQdrant FundamentalsDatabase OptimizationDocumentationSearch EngineeringMultivectors and Late InteractionMultivector Representations for Reranking in QdrantTime: 30 minLevel: IntermediateMultivector Representations are one of the most powerful features of Qdrant. However, most people don’t use them effectively, resulting in massive RAM overhead, slow inserts, and wasted compute.In this tutorial, you’ll discover how to effectively use multivector representations in Qdrant.What are Multivector Representations?In most vector engines, each document is represented by a single vector - an approach that works well for short texts but often struggles with longer documents. Single vector representations perform pooling of the token-level embeddings, which obviously leads to losing some information.Multivector representations offer a more fine-grained alternative where a single document is represented using multiple vectors, often at the token or phrase level. This enables more precise matching between specific query terms and relevant parts of the document. Matching is especially effective in Late Interaction models like ColBERT, which retain token-level embeddings and perform interaction during query time leading to relevance scoring.As you will see later in the tutorial, Qdrant supports multivectors and thus late interaction models natively.Why Token-level Vectors are UsefulWith token-level vectors, models like ColBERT can match specific query tokens to the most relevant parts of a document, enabling high-accuracy retrieval through Late Interaction.In late interaction, each document is converted into multiple token-level vectors instead of a single vector. The query is also tokenized and embedded into various vectors. Then, the query and document vectors are matched using a similarity function: MaxSim. You can see how it is calculated here.In traditional retrieval, the query and document are converted into single embeddings, after which similarity is computed. This is an early interaction because the information is compressed before retrieval.What is Rescoring, and Why is it Used?Rescoring is two-fold:Retrieve relevant documents using a fast model.Rerank them using a more accurate but slower model such as ColBERT.Why Indexing Every Vector by Default is a ProblemIn multivector representations (such as those used by Late Interaction models like ColBERT), a single logical document results in hundreds of token-level vectors. Indexing each of these vectors individually with HNSW in Qdrant can lead to:High RAM usageSlow insert times due to the complexity of maintaining the HNSW graphHowever, because multivectors are typically used in the reranking stage (after a first-pass retrieval using dense vectors), there’s often no need to index these token-level vectors with HNSW.Instead, they can be stored as multi-vector fields (without HNSW indexing) and used at query-time for reranking, which reduces resource overhead and improves performance.For more on this, check out Qdrant’s detailed breakdown in our Scaling PDF Retrieval with Qdrant tutorial.With Qdrant, you have full control of how indexing works. You can disable indexing by setting the HNSW m parameter to 0:from qdrant_client import QdrantClient, models\n\nclient = QdrantClient("http://localhost:6333")\ncollection_name = "dense_multivector_demo"\nclient.create_collection(\n collection_name=collection_name,\n vectors_config={\n "dense": models.VectorParams(\n size=384,\n distance=models.Distance.COSINE\n # Leave HNSW indexing ON for dense\n ),\n "colbert": models.VectorParams(\n size=128,\n distance=models.Distance.COSINE,\n multivector_config=models.MultiVectorConfig(\n comparator=models.MultiVectorComparator.MAX_SIM\n ),\n hnsw_config=models.HnswConfigDiff(m=0) # Disable HNSW for reranking\n )\n }\n)\nBy disabling HNSW on multivectors, you:Save compute.Reduce memory usage.Speed up vector uploads.How to Generate Multivectors Using FastEmbedLet’s demonstrate how to effectively use multivectors using FastEmbed, which wraps ColBERT into a simple API.Install FastEmbed and Qdrant:pip install qdrant-client[fastembed]>=1.14.2\nStep-by-Step: ColBERT + Qdrant SetupEnsure that Qdrant is running and create a client:from qdrant_client import QdrantClient, models\n\n# 1. Connect to Qdrant server\nclient = QdrantClient("http://localhost:6333")\n1. Encode DocumentsNext, encode your documents:from fastembed import TextEmbedding, LateInteractionTextEmbedding\n# Example documents and query\ndocuments = [\n "Artificial intelligence is used in hospitals for cancer diagnosis and treatment.",\n "Self-driving cars use AI to detect obstacles and make driving decisions.",\n "AI is transforming customer service through chatbots and automation.",\n # ...\n]\nquery_text = "How does AI help in medicine?"\n\ndense_documents = [\n models.Document(text=doc, model="BAAI/bge-small-en")\n for doc in documents\n]\ndense_query = models.Document(text=query_text, model="BAAI/bge-small-en")\n\ncolbert_documents = [\n models.Document(text=doc, model="colbert-ir/colbertv2.0")\n for doc in documents\n]\ncolbert_query = models.Document(text=query_text, model="colbert-ir/colbertv2.0")\n2. Create a Qdrant collectionThen create a Qdrant collection with both vector types. Note that we leave indexing on for the dense vector but turn it off for the colbert vector that will be used for reranking.collection_name = "dense_multivector_demo"\nclient.create_collection(\n collection_name=collection_name,\n vectors_config={\n "dense": models.VectorParams(\n size=384,\n distance=models.Distance.COSINE\n # Leave HNSW indexing ON for dense\n ),\n "colbert": models.VectorParams(\n size=128,\n distance=models.Distance.COSINE,\n multivector_config=models.MultiVectorConfig(\n comparator=models.MultiVectorComparator.MAX_SIM\n ),\n hnsw_config=models.HnswConfigDiff(m=0) # Disable HNSW for reranking\n )\n }\n)\n3. Upload Documents (Dense + Multivector)Now upload the vectors, with batch_size=8. We do not have many documents, but batching is always recommended.points = [\n models.PointStruct(\n id=i,\n vector={\n "dense": dense_documents[i],\n "colbert": colbert_documents[i]\n },\n payload={"text": documents[i]}\n ) for i in range(len(documents))\n]\nclient.upload_points(\n collection_name="dense_multivector_demo", \n points=points, \n batch_size=8\n)\nQuery with Retrieval + Reranking in One CallNow let’s run a search:results = client.query_points(\n collection_name="dense_multivector_demo",\n prefetch=models.Prefetch(\n query=dense_query,\n using="dense",\n ),\n query=colbert_query,\n using="colbert",\n limit=3,\n with_payload=True\n)\nThe dense vector retrieves the top candidates quickly.The Colbert multivector reranks them using token-level MaxSim with fine-grained precision.Returns the top 3 results.ConclusionMultivector search is one of the most powerful features of a vector database when used correctly. With this functionality in Qdrant, you can:Store token-level embeddings natively.Disable indexing to reduce overhead.Run fast retrieval and accurate reranking in one API call.Efficiently scale late interaction.Combining FastEmbed and Qdrant leads to a production-ready pipeline for ColBERT-style reranking without wasting resources. You can do this locally or use Qdrant Cloud. Qdrant offers an easy-to-use API to get started with your search engine, so if you’re ready to dive in, sign up for free at Qdrant Cloud and start building.Was this page useful?\n\nYes\n\n\n\nNoThank you for your feedback! 🙏We are sorry to hear that. 😔 You can edit this page on GitHub, or create a GitHub issue.On this page:Multivector Representations for Reranking in QdrantWhat are Multivector Representations?Why Token-level Vectors are UsefulWhat is Rescoring, and Why is it Used?Why Indexing Every Vector by Default is a ProblemHow to Generate Multivectors Using FastEmbedStep-by-Step: ColBERT + Qdrant Setup1. Encode Documents2. Create a Qdrant collection3. Upload Documents (Dense + Multivector)Query with Retrieval + Reranking in One CallConclusion\nView as Markdown\nEdit on Github\nCreate an issue\nReady to try Qdrant?Ready to get started with Qdrant?Start Free© 2026 Qdrant.Terms\nPrivacy Policy\nImpressum\nRecruitment Privacy Policy\nCookie Consent","metadata":{"truncated":false},"title":"https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/ (text/html; charset=UTF-8)","time":{"start":1788986619492,"end":1788986619830}},"id":"prt_g001a087e9740e001NTab6Ygur","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e92102001NYiEiBq87"}} +{"type":"tool_use","timestamp":1788986621747,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_db19d2f4243a4984b5483a72","state":{"status":"completed","input":{"format":"text","url":"https://weaviate.io/blog/late-interaction-overview"},"output":"An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ColQwen | Weaviate\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n← Back to BlogsSkip to main contentIntroducing Engram by Weaviate - now GA - Read our Blog Product▼Solutions▼EnterpriseDevelopers▼Company▼PricingContactLog inTry now☰An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ColQwenApril 9, 2025 · 21 min readLeonie MonigattiMachine Learning EngineerDanny WilliamsMachine Learning EngineerVictoria SlocumMachine Learning Engineer\n\nWhy Late Interaction?​\nLate interaction retrieval models are changing how we find and retrieve information. These models allow for semantically rich interactions that enable a precise retrieval process across different modalities of unstructured data, including text and images. By decomposing query and document embeddings into lower-level representations, late interaction models use multi-vector retrieval and improve both accuracy and scalability compared to no-interaction and full-interaction dense retrieval models.\nLate interaction models can be used across various retrieval tasks, helping researchers and practitioners find relevant documents in large document collections, such as financial documents or legal contracts. These models are particularly helpful in downstream tasks requiring efficient retrieval processes, such as search engines or Generative AI applications, such as Retrieval-Augmented Generation (RAG) pipelines, where semantic precision can improve information extraction and understanding.\n3 Types of Interaction in Dense Retrieval Models​\nDense retrieval models can be categorized based on their type of “interaction”. A dense retrieval model is a model that uses some type of neural network architecture to retrieve relevant documents for a search query. In this context, “interaction” refers to the process of assessing how well a document matches a given search query by comparing their representations. This section discusses the different types of interactions in dense retrieval models.\nNo-interaction Retrieval Models​\nTraditional methods for retrieval commonly use “no-interaction” retrieval models. In this case, the search query and documents are processed separately:\n\nDuring the indexing phase, one dense vector embedding is precomputed for each document. The document is first split up into separate tokens, all tokens are passed together through a neural network model, typically a transformer model, to obtain separate dense embeddings for each token. Finally, the token embeddings are calculated into a single vector representation via pooling.\nDuring the online querying phase, one vector is computed for the search query to search for similar documents from the pre-computed document embeddings with a similarity metric (e.g., dot product, cosine similarity).\n\nAdvantages of no-interaction retrieval models are primarily that they are fast and computationally efficient. The vector representations of the documents are already pre-computed and indexed offline, and at query time, only the query embeddings have to be computed. This makes this solution scalable for large-scale information retrieval systems and vector search applications.\nDisadvantages of no-interaction retrieval models lie in the lack of interaction between the search query and the documents. Because the search query and the documents are processed separately and compressed into single vector embeddings, this approach can miss contextual nuances. Any contextual nuances shared from one token to another have to be compressed before the comparison takes place - meaning similarity is compared between the whole document and the whole query, rather than on a token-by-token level.\nThese characteristics make no-interaction models great for first-stage retrieval, where candidates are retrieved from a large document collection, like in vector search. Examples of no-interaction retrieval models are bi-encoders, such as OpenAI’s text-embedding-3-small.\n\nFull interaction Retrieval Models​\nFull interaction retrieval models process the search query and documents together. That’s why they are sometimes also called early interaction or all-to-all interaction retrieval models. In contrast to no-interaction models, all-to-all interaction models process everything at query time. For this, the query is concatenated with each document, and then they are processed together for full cross-attention.\nAdvantages of full interaction retrieval models stem from them being contextually rich, meaning they effectively capture nuanced contextual relationships between the search query and the document.\nDisadvantages of full interaction models are due to them being extremely computationally expensive because all documents have to be processed at query time. That’s why full interaction models are not scalable for large amounts of documents.\nThese characteristics make full interaction models great for second-stage retrieval, like reranking a curated set of candidate documents. Examples of full interaction retrieval models are BERT and other models with cross-encoder architectures, which are often popular in reranking models.\n\nLate interaction retrieval models​\nWhilst no-interaction models are fast but can be inaccurate, all-to-all interaction models are accurate but slow, late interaction retrieval models promise to be both. Late interaction models are inspired by both of the above retrieval models.\nSimilar to no-interaction retrieval models, late interaction models have a dual encoder architecture and precompute embeddings for each document offline (in advance of the interaction stage). However, in late interaction, the embeddings that are stored are for each token, rather than a single embedding for the whole document. Instead of pooling these token embeddings, the pooling mechanism is replaced with the late interaction mechanism. That means that the token-level multi-vector embeddings are kept to calculate the interaction between the search query and document multi-vector representations, similar to full interaction retrieval models, but at a later stage.\nAdvantages of late interaction retrieval models are that they are both scalable and contextually rich.\nDisadvantages of late interaction are related to their storage requirements - they require an embedding for each token, which requires a lot more storage for a complete set of vectors.\nHowever, token embeddings can be stored at a much lower dimension and quantization level to decrease computational costs and increase storage efficiency - mitigating some of these issues. For example, in ColBERTv2, aggressive quantization is performed to reduce the size of each vector from 256 bytes to 36 bytes (2-bit compression) or even 20 bytes (1-bit compression), whilst maintaining a good level of accuracy (see the ColBERTv2 section later).\nExamples of late interaction retrieval models are ColBERT, ColPali, and ColQwen, which are discussed in the following sections.\n\nHow does the late interaction mechanism work?​\nThe late interaction mechanism calculates the maximum similarity score between the query tokens and the document tokens. It requires lower-level representations of input documents or images, such as their tokens (word parts) or image patches (small image/document sections), and uses these for comparison instead of a global document representation. Late interaction was first introduced in the ColBERT paper.\n\nLet’s say we have a text query and text documents, for simplicity of explanation. Instead of pooling the vector embeddings into a single vector representation for the query and the document, we keep the token-level embeddings and apply the MaxSim operator to calculate the relevance scores:\n\nFor each query token, we compute its similarity score (e.g., dot product or cosine similarity) with every document token.\nThen, for each query token, we keep only the maximum similarity score (e.g., maximum dot product or maximum cosine similarity).\nFinally, we sum all of those token-level maximum similarity scores into a final relevance score.\n\nYour browser does not support the video tag.\nWhat is ColBERT?​\nThe ColBERT paper, released in 2020, introduced both the original ColBERT model and the late interaction mechanism. ColBERT stands for “Contextualized Late Interaction over BERT”. It is a model fine-tuned on the BERT language model, which was one of the first big transformer models in NLP. ColBERT however uses the late interaction mechanism instead of the typical all-to-all interaction that BERT uses, and is specialized towards generating contextualized embeddings for queries and documents. Since BERT is a text-only model (not multimodal), ColBERT is also a text-only model.\nFun fact: It’s pronounced /koʊlˈbɛər after Stephen Colbert in reference to his Late Show.\nColBERT Model Architecture​\nSince ColBERT is a fine-tune of BERT, its architecture is almost identical to BERT. BERT-base was trained with 110M parameters (340M for BERT-large), and different variations of ColBERT were trained from BERT-base and BERT-large on different training datasets. The version of ColBERT you’ll find on Hugging Face is fine-tuned from BERT-base and has around 110M parameters.\nHowever, there are a few differences between ColBERT and BERT. Firstly, BERT-base used a fixed embedding dimension of 768 (1024 for BERT-large), whereas ColBERT uses a smaller representation dimension of 128 for each vector in an effort to reduce the storage requirements of each vector. This is achieved via a projection layer in ColBERT to make the final output a 128-dimensional vector.\nThe same fine-tuned BERT model is used in ColBERT as both the query encoder and the document encoder separately, but two additional special tokens, [Q] and [D], are prepended to the query and document respectively to 'mark' them as such. This marker is included in model training, so that the model learns to distinguish the query and document via these special tokens.\nA full forward pass of ColBERT is given in order by:\n\nTokenize the query/document (and include special token [Q] or [D])\nPass the tokens through a 110M fine-tuned BERT-base transformer model\nProject the final BERT representations with a special projection layer to reduce the embedding dimension to 128 instead of 768\nCompare all tokens between query and document using late interaction and MaxSim.\n\n\nColBERT v2 and its improvements​\nWhilst ColBERT introduced the concept of late interaction, ColBERTv2 refined it. The main downside of ColBERT is the storage requirements - needing a full vector for each token in each document. ColBERTv2 improves over the original ColBERT’s weakness of huge memory storage requirements, whilst also improving the quality of retrieval.\nThe increased quality of ColBERTv2 comes from a process called denoised supervision, which involves distillation (passing on knowledge) from a larger teacher model and hard negative mining (showing similar but incorrect retrievals to a given query).\nDistillation was from the teacher model, MiniLM. This cross-encoder model uses all-to-all interaction, like BERT, which means it has a high accuracy but is more inefficient to use in practice. Distillation means that this model can be used to teach ColBERTv2, whilst ColBERTv2 retains its increased computational efficiency thanks to late interaction.\nHard negative mining involves framing the retrieval with documents that are similar, but not relevant to, the query, so the model learns to distinguish between the correct document, and documents similar to this correct one. This reduces the number of false negatives ColBERTv2 will retrieve.\nThe improved storage efficiency of ColBERTv2 comes from residual compression. Residual compression is a technique which stores ‘centroids’ in high precision (more accurate vectors with high storage cost), and ‘residual’ vectors in low precision (less accurate vectors with lower storage cost). Each token embedding is a combination of a centroid and its own, unique, residual vector. These vector centroids take up more space but there are fewer of them; according to the ColBERTv2 paper, they recommend using\n⌊16×ndocs×dembedding⌋\\lfloor 16 \\times \\sqrt{n_{\\text{docs}} \\times d_{\\text{embedding}}} \\rfloor⌊16×ndocs​×dembedding​​⌋\nmany vectors, which is a value proportional to the number of overall embeddings in the dataset.\nUsing this residual compression technique reduces the space footprint massively - the MS MARCO training dataset had a 154GB index on ColBERTv1, whereas on COLBERTv2 this technique allowed it to be stored as low as 16GB for 1-bit compression, or 25GB for 2-bit compression. The space footprint from ColBERTv2 was reduced by 6-10x compared to ColBERTv1, and brings this value closer to what a typical single vector model would require for this dataset.\nAdvantages and Limitations of ColBERT​\nOne advantage of ColBERT is its impact on retrieval performance. Because it’s scalable and contextually rich, it can improve retrieval accuracy and, therefore, overall performance in RAG applications and ranking pipelines compared to typical dense vector embedding models.\nAnother positive side-effect of keeping token-level embeddings for similarity scoring in ColBERT is an added feature of explainability. Similar to keyword matching, you can get more interpretable insights for your vector search results because you can see which tokens have a high similarity score between the search query and the relevant document.\n\nHowever, the multi-vector approach of the original ColBERT model has one big disadvantage of the increased space footprint. Because with ColBERT you now have one vector embedding for each token in a document instead of one vector embedding for one document, the storage requirements and memory usage balloon up, but this can be mitigated somewhat with the quantization methods introduced by ColBERTv2.\nLet’s compare the storage requirements of dense vector embeddings and multi-vector embeddings. Assume that each document consists of 100 tokens, and we are storing a variable number of documents. The table below gives the storage cost, in megabytes, to store all of the documents as their respective embeddings.\nThe storage costs calculated assume that each value in the single vector\nembedding is quantized slightly at 16-bits per dimension, and the\nmulti-vector embedding is heavily quantized to 2-bits per dimension, with\nthe centroids assumed as 16-bit precision, and the number of centroids is\ngiven by the formula in the previous section.Single Vector EmbeddingsMulti Vector Embeddings(ColBERTv2 quantization)d=768d=1024d=1536d=64d=96d=128Number of Documents (n)(100 tokens per document)n=1000.15 MB0.20 MB0.31 MB0.36 MB0.55 MB0.73 MBn=5000.77 MB1.02 MB1.54 MB1.26 MB1.89 MB2.52 MBn=10001.54 MB2.05 MB3.07 MB2.25 MB3.37 MB4.50 MBn=20003.07 MB4.10 MB6.14 MB4.12 MB6.17 MB8.23 MBn=1000015.36 MB20.48 MB30.72 MB18.05 MB27.07 MB36.10 MB\nEven with heavy quantization, storing 128-dimensional vectors with ColBERTv2 costs significantly more than storing a high-dimensional single vector for the entire document/chunk. As the size of the documents and the number of tokens scale up, the storage cost will also balloon, which could be unsustainable for some scenarios. However, the increased contextual richness and retrieval performance may well be worth the extra storage.\n\nUse Cases of ColBERT​\nBecause of its improved correlation of textual nuances, ColBERT is great for retrieval tasks that require a higher level of accuracy, like legal text-based RAG pipelines or financial document verification.\nYou can find a RAG code example with ColBERT in Python in our documentation.\nWhat are ColPali and ColQwen?​\nColPali and ColQwen are multimodal late interaction retrieval models that use vision language models (VLMs) instead of text-only models. ColPali stands for Contextualized Late Interaction over PaliGemma, while ColQwen stands for Contextualized Late Interaction over Qwen2 (that’s why it’s also called ColQwen2).\nUnlike conventional approaches to PDF retrieval that require Optical Character Recognition (OCR) and advanced chunking to separate different content formats (i.e textual content and visual content, like graphs or images) when doing complex document processing, ColPali and ColQwen treat the entire PDF as an image, and thus these models extend ColBERT's concept to visual content.\n\nColPali and ColQwen process complex documents as images through a similar pipeline:\n\nDocument Image Transformation: The entire document is treated as an image rather than separating textual content and visual content, such as text, tables, and graphics\nPatch Generation: The document image is divided into a series of uniform image patches of predefined dimensions\nVision Encoding: Each image patch is processed by a vision encoder model, transforming visual information into initial image embeddings\nContextualization: The patch embeddings are passed to a VLM (PaliGemma in the case of ColPali, Qwen2 in the case of ColQwen), which contextualizes them within the document's overall structure\nVector Space Projection: The contextualized patch embeddings are mapped into a text vector space, creating representations in the same search space that maintain awareness of surrounding patches\nQuery Processing: User queries are embedded by the language model component to create token-level embeddings in the same vector space as the document patches\nLate Interaction Matching: Query tokens are matched to document patches using a MaxSim operator\n\nThis multimodal late interaction approach preserves contextual relationships between patches, similar to how tokens in ColBERT have context within their entire sentence. So when examining specific query terms, the models can identify and highlight the most relevant visual components and image patches in the document.\nColPali Model Architecture​\nColPali was created in a similar way to ColBERT. What BERT is to ColBERT, PaliGemma is to ColPali. Essentially, instead of using BERT as the query and document encoder, ColPali uses the PaliGemma Vision LLM, which is separated into two parts for the query and document. Since queries will always be text-only, it uses Gemma (the language-only part of PaliGemma) as the query encoder and PaliGemma (the full vision model) as the vision encoder. Just like ColBERT, ColPali stores the pre-computed document vectors offline, and uses late interaction to compare to online embedded queries at runtime. ColPali has:\n\n~3B parameters\n128 embedding dimension size\nmax 1024 patch size\n\n\nColQwen Model Architecture​\nColQwen is extremely similar to ColPali, with a different vision language model, Qwen2 Vision LLM, as the encoder. ColQwen uses Qwen2 LM as the query encoder and Qwen2-VL as the vision encoder. Whilst having a similar architecture to ColPali, the details are different due to using a completely different encoder model:\n\n~2B parameters\n128 embedding dimension\nmax 768 patch size\n\nAdditionally, ColQwen’s vision language backbone model is licensed under a permissive apache2.0 license.\n\nAdvantages and Limitations of ColPali and ColQwen​\nThe advantages of multimodal late interaction retrieval models like ColPali and ColQwen are that they allow for RAG applications without a complex preprocessing or chunking strategy for the PDF documents. Both text and images in PDF documents can be simply treated together as 'screenshots', which the vision models in ColPali and ColQwen can see and process. This also means figures in documents can be placed contextually with the text of the document, and these documents are processed similarly to how a human would read them.\nBut while ColPali and ColQwen both simplify the normally complex pipeline for PDF document processing, they also requires more resources to store all individual image patch embeddings, even with compression mechanisms, similar to ColBERT.\nUse Cases of ColPali and ColQwen​\nBecause of their multimodal capabilities ColPali and ColQwen are great for retrieval pipelines that process complex documents like PDFs, containing different components like text, images, graphs, tables, and formulas.\nYou can find a multimodal RAG over PDF documents code example with ColPali in Python in our recipes repository on GitHub.\nSummary​\nThis article discussed the late interaction mechanism and its retrieval models, such as ColBERT, ColPali, and ColQwen. In contrast to no-interaction models and full-interaction models, which are either effective or scalable, late interaction models are fast and effective retrieval models. That’s why they are a great fit for applications that require an effective and efficient retrieval process.\nThe added multi-modality in ColPali and ColQwen allows us to build RAG pipelines over complex documents, such as PDF files, by converting them into images. Although late interaction retrieval models require more memory because of the multi-vector embeddings, they also offer a richer contextual understanding of both text and image files. That’s why multimodal late interaction retrieval models are especially useful in multimodal rag pipelines for processing complex documents, such as PDF files.\nIf you want to start playing around with the discussed late interaction models, you can find code examples here:\n\nRAG code example with ColBERT in our documentation.\nMultimodal RAG over PDF documents code example with ColPali in Python in our recipes repository on GitHub.\n\nFAQ​\nWhat’s the difference between ColBERT and dense vector methods?​\nThe difference between ColBERT and dense vector methods is that these traditional dense methods pool token-wise embeddings into a single representation while ColBERT embeddings keep the token-wise representations in a multi-vector embedding. It would be inefficient to keep token-wise representations from traditional dense embedding models because they are not trained for this purpose, whereas multi-vector embedding models like ColBERT are. The benefit of keeping the token-wise representation is that it allows for a more detailed understanding of the semantic similarity between the query and the search results. Additionally, there’s no loss of context as embeddings remain at the token level.\nWhat’s the difference between BERT vs. ColBERT?​\nThe difference between BERT and ColBERT is that the offline computation of document embeddings allows ColBERT to be faster at run-time compared to BERT. BERT requires embedding queries and documents together in order to provide a comparison, whilst ColBERT has the document embeddings pre-computed and stored.\nOn a side note, ColBERT uses a fine-tuned version of BERT as encoders for both the query and the documents.\nWhat’s the difference between ColBERT vs. ColBERTv2?​\nThe difference between ColBERT and ColBERTv2 is the improved storage efficiency and higher retrieval effectiveness in the newer model. Any multi-vector model suffers from high storage costs due to the requirement of storing multiple vectors per document, which scales highly with the number of tokens in a document. ColBERTv2 alleviates the storage issues by heavily quantizing the token vector embeddings, and storing a subset of ‘nearby’ centroid vectors in higher precision. Combining the centroids with the quantized vectors gives a very good approximation to the true token embedding whilst minimizing the storage costs of each vector.\nAdditionally, ColBERTv2 gains strong retrieval performance over ColBERT by distilling knowledge from a larger, more impractically sized model (MiniLM), as well as by training the model to learn to distinguish between similar but incorrect documents and the correct documents for a given query.\nWhat’s the difference between ColBERT vs. ColPali and ColQwen?​\nThe difference between ColBERT vs. ColPali and ColQwen lies in the modalities these retrieval models are able to handle. Because ColBERT uses BERT, it is able to handle only text. In contrast, ColPali and ColQwen use VLMs and are therefore multimodal and can handle both text and images. While the search query for all models is a text input, the type of documents to search over is different between the models. ColBERT processes documents in text format, while ColPali and ColQwen process images, including PDF documents treated as images.\nWhat’s the difference between ColPali vs. ColQwen?​\nThe main difference between ColPali and ColQwen is that ColQwen uses the Qwen vision LM, whereas ColPali uses the PaliGemma VLM (and Gemma LM for embeddings). Additionally, the Qwen2 VLM has 1 billion parameters fewer than the PaliGemma VLM, making ColQwen a smaller model. ColQwen also has a smaller image patch size, which can result in lower storage and computational costs.\nResources​\n\nColBERT paper: https://arxiv.org/abs/2004.12832\nColBERT v2 paper: https://arxiv.org/abs/2112.01488\nColPali paper: https://arxiv.org/abs/2407.01449\nPaliGemma paper: https://arxiv.org/pdf/2407.07726\n\n\nReady to start building?​\nCheck out the Quickstart tutorial, or sign up for a free Weaviate Cloud account.\n\nGitHubForumX (Twitter)Don't want to miss another blog post?Sign up for our bi-weekly newsletter to stay updated! By submitting, I agree to the Terms of Service and Privacy Policy.Tags:conceptssearchEdit this pageNewer postIntroducing the Weaviate Personalization AgentOlder postWeaviate 1.30 ReleaseWhy Late Interaction?3 Types of Interaction in Dense Retrieval ModelsNo-interaction Retrieval ModelsFull interaction Retrieval ModelsLate interaction retrieval modelsHow does the late interaction mechanism work?What is ColBERT?ColBERT Model ArchitectureColBERT v2 and its improvementsAdvantages and Limitations of ColBERTUse Cases of ColBERTWhat are ColPali and ColQwen?ColPali Model ArchitectureColQwen Model ArchitectureAdvantages and Limitations of ColPali and ColQwenUse Cases of ColPali and ColQwenSummaryFAQWhat’s the difference between ColBERT and dense vector methods?What’s the difference between BERT vs. ColBERT?What’s the difference between ColBERT vs. ColBERTv2?What’s the difference between ColBERT vs. ColPali and ColQwen?What’s the difference between ColPali vs. ColQwen?ResourcesReady to start building?Get the developer newsletterProduct updates, how-tos, community spotlights, and more. 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You can unsubscribe at any time.ProductVector DatabaseEmbeddingsQuery AgentIntegrationsEngramProduct PreviewsShared CloudDedicated CloudWeaviate AssuranceDigitalOceanPricingSolutionsRAGHybrid SearchAgentic AICost Performance OptimizationCase StudiesWeaviate PlaygroundDevelopersWeaviate Database DocsWeaviate Cloud DocsWeaviate Deployment DocsQuery Agent DocsLLM / Agent guidesGitHubLearning CenterBlogAcademyKnowledge CardsPaper ReviewsPodcastsEvents & WebinarsWeaviate Hero ProgramForumCompanyCompanyCareersRemotePlaybookInvestorsContact UsPartners OverviewAWSGoogle CloudSnowflakeDatabricksSecurityTerms & PoliciesReport a VulnerabilityPreferencesCookie Settings","metadata":{"truncated":false},"title":"https://weaviate.io/blog/late-interaction-overview (text/html; charset=UTF-8)","time":{"start":1788986619496,"end":1788986621744}},"id":"prt_g001a087e974b3001zkHRcZ4nR","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e92102001NYiEiBq87"}} +{"type":"step_finish","timestamp":1788986621771,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e97f32001D6Hm5Re2W","reason":"tool-calls","snapshot":"4ae0c19520a96aaf57696fbcfa66c91e9cae9414","messageID":"msg_g001a087e92102001NYiEiBq87","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":50841,"input":32263,"output":978,"reasoning":0,"cache":{"write":0,"read":17600}},"cost":0}} +{"type":"step_start","timestamp":1788986627663,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e9964d0018JVkTtsud","messageID":"msg_g001a087e97f7a0010EXFLwCm0","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"0047fef42646649be64056e4711eb733b0f2b176","type":"step-start"}} +{"type":"tool_use","timestamp":1788986628698,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_45c4fe9e531d4319bd472c18","state":{"status":"completed","input":{"format":"text","url":"https://raw.githubusercontent.com/qdrant/qdrant/master/README.md"},"output":"

\n \n \n \n \"Qdrant\"\n \n

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\n Vector Search Engine for the next generation of AI applications\n

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\n \"Tests\n \"OpenAPI\n \"Apache\n \"Discord\"\n \"Roadmap\n \"Qdrant\n

\n\n**Qdrant** (read: _quadrant_) is a vector similarity search engine and vector database.\nIt provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.\nQdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.\n\nQdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See [benchmarks](https://qdrant.tech/benchmarks/).\n\nWith Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!\n\nQdrant is also available as a fully managed **[Qdrant Cloud](https://cloud.qdrant.io/)** ⛅ including a **free tier**.\n\n

\nQuick Start • Agent Skills • Client Libraries • Demo Projects • Integrations • Contact\n\n\n

\n\n## Getting Started\n\n### Agent Skills\n\nQdrant provides a collection of ready-to-use [agent skills](https://github.com/qdrant/skills) that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.\n\n### Client-Server\n\nTo experience the full power of Qdrant locally, run the container with this command:\n\n```bash\ndocker run -p 6333:6333 qdrant/qdrant\n```\n\nNote that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to [secure your instance](https://qdrant.tech/documentation/security/#secure-your-instance).\n\nNow you can connect to the server with any [client](#clients). For example, using Python:\n\n```python\nfrom qdrant_client import QdrantClient\n\nclient = QdrantClient(url=\"http://localhost:6333\")\n```\n\nBefore deploying Qdrant to production, be sure to read our [installation](https://qdrant.tech/documentation/installation/) and [security](https://qdrant.tech/documentation/security/) guides.\n\n### Clients\n\nQdrant offers the following client libraries to help you integrate it into your application stack:\n\n- Official:\n - [Go client](https://github.com/qdrant/go-client)\n - [Rust client](https://github.com/qdrant/rust-client)\n - [JavaScript/TypeScript client](https://github.com/qdrant/qdrant-js)\n - [Python client](https://github.com/qdrant/qdrant-client)\n - [.NET/C# client](https://github.com/qdrant/qdrant-dotnet)\n - [Java client](https://github.com/qdrant/java-client)\n- Community:\n - [Kotlin](https://github.com/NaCode-Studios/Kdrant)\n - [PHP](https://github.com/hkulekci/qdrant-php)\n\n### Qdrant Edge\n\n[Qdrant Edge](https://qdrant.tech/documentation/edge/) is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.\n\nTo get started with Qdrant Edge from Python or Rust, initialize an instance of EdgeShard, which exposes methods to manage data, query it, and restore snapshots. For example:\n\n```python\nfrom qdrant_edge import Distance, EdgeConfig, EdgeVectorParams, EdgeShard, Point, UpdateOperation\n\nshard = EdgeShard.create(\"./shard\", EdgeConfig(\n vectors={\"my-vector\": EdgeVectorParams(size=4, distance=Distance.Cosine)}\n))\nshard.update(UpdateOperation.upsert_points([\n Point(id=1, vector={\"my-vector\": [0.1, 0.2, 0.3, 0.4]}, payload={\"color\": \"red\"})\n]))\n```\n\n### Where Do I Go from Here?\n\n- [Quick Start Guide](https://qdrant.tech/documentation/quickstart/)\n- Detailed [Documentation](https://qdrant.tech/documentation/)\n- Take the [Qdrant Essentials course](https://qdrant.tech/course/essentials/)\n- Follow [this tutorial](https://qdrant.tech/documentation/tutorials-basics/search-beginners/) to create a semantic search engine with Qdrant\n\n## Demo Projects\n\n### Discover Semantic Text Search 🔍\n\nUnlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. [Try it online!](https://qdrant.to/semantic-search-demo)\n\n### Explore Similar Image Search - Food Discovery 🍕\n\nThere's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name. [Check it out!](https://qdrant.to/food-discovery)\n\n### Master Extreme Classification - E-Commerce Product Categorization 📺\n\nEnter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. [Play with it online!](https://qdrant.to/extreme-classification-demo)\n\n## API\n\n### REST\n\nQdrant provides a REST API with an [OpenAPI 3.0 specification](https://api.qdrant.tech/), enabling client generation for virtually any framework or programming language.\n\nYou can also download the raw [OpenAPI definitions](https://github.com/qdrant/qdrant/blob/master/docs/redoc/master/openapi.json).\n\n### gRPC\n\nFor faster, production-tier searches, Qdrant also provides a [gRPC interface](https://qdrant.tech/documentation/interfaces/#grpc-interface).\n\n## Features\n\n### Dense, Sparse, and Multi Vector Search\n\nQdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models like ColBERT.\n\n### Filtering on Payload\n\nAttach any JSON payload to your vectors and filter on it using a rich set of conditions—keyword matching, full-text, numeric ranges, geo-locations, and more—combined with `should`, `must`, and `must_not` clauses.\n\n### Hybrid Search\n\nCombine multiple vectors in a single query to get the best of semantic understanding and keyword precision, with results merged via configurable fusion strategies, such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF).\n\n### Vector Quantization and On-Disk Storage\n\nBuilt-in quantization cuts RAM usage by up to 97% and lets you tune the trade-off between search speed and precision.\n\n### Distributed Deployment\n\nScale horizontally with sharding and replication, and update or resize collections with zero downtime.\n\n### Highlighted Features\n\n* **Faceting** - aggregate search results by payload values.\n* **Recommendation** - use positive and negative examples to find similar points.\n* **Discovery** - constrain search to a specific region of the vector space.\n* **Search Relevance Tuning** - tools for adjusting search results, such as Maximal Marginal Relevance (MMR) and the Relevance Feedback Query.\n* **Multitenancy** - scalable partitioning of data for multi-user environments.\n* **Observability** - comprehensive metrics, telemetry, and audit logging for monitoring and debugging.\n* **Query Planning and Payload Indexes** - leverages stored payload information to optimize query execution strategy.\n* **SIMD Hardware Acceleration** - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.\n* **GPU Support** - for accelerated indexing, with support for NVIDIA and AMD GPUs.\n* **Async I/O** - uses `io_uring` to maximize disk throughput utilization even on network-attached storage.\n* **Write-Ahead Logging** - ensures data persistence with update confirmation, even during power outages.\n\n### Web UI\n\nWeb UI provides a visual way to interact with your data and monitor the health of your deployment. It enables you to explore your collections, manage data, interact with the REST API, and more.\n\n

\"Qdrant

\n\n## Integrations\n\nQdrant integrates with the tools you're already using across every stage of your AI stack. You can connect to embedding providers, AI application frameworks, and data pipeline tools, as well as observability platforms for monitoring and tracing your vector search in production. No-code and low-code automation platforms are supported too. Refer to the [Ecosystem page](https://qdrant.tech/documentation/ecosystem/) for the complete list.\n\n## Contributing\n\nWe are happy to receive your contributions! Before opening a pull request, please read our [Contributing Guide](docs/CONTRIBUTING.md).\n\n> [!IMPORTANT]\n> Our development branch is `dev`, not `master`. Please fork the repo, branch from `dev`, and open your pull request against `dev`. PRs targeting `master` will be asked to retarget.\n\n## Contacts\n\n- Have questions? Join our [Discord channel](https://qdrant.to/discord) or mention [@qdrant_engine on X](https://qdrant.to/twitter)\n- Want to stay in touch with the latest releases? Subscribe to our [Newsletters](https://qdrant.tech/subscribe/)\n- Looking for a managed cloud? Check [pricing](https://qdrant.tech/pricing/). Need something personalized? We're at [info@qdrant.tech](mailto:info@qdrant.tech)\n\n## License\n\nQdrant is licensed under the Apache License, Version 2.0. View a copy of the [License file](https://github.com/qdrant/qdrant/blob/master/LICENSE).\n","metadata":{"truncated":false},"title":"https://raw.githubusercontent.com/qdrant/qdrant/master/README.md (text/plain; charset=utf-8)","time":{"start":1788986628451,"end":1788986628697}},"id":"prt_g001a087e9964e001Vj0vjVKTo","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e97f7a0010EXFLwCm0"}} +{"type":"tool_use","timestamp":1788986628934,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"webfetch","callID":"call_834ef51273c94e19a2f60439","state":{"status":"completed","input":{"format":"text","url":"https://github.com/qdrant/qdrant/blob/master/README.md"},"output":"qdrant/README.md at master · qdrant/qdrant · GitHub\n\n\n\n \n \n \n \n\n \n \n\n\n \n\n\n \n\n\n \n \n\n \n \n\n \n\n\n\n\n\n\n\n \n\n \n\n\n\n\n \n\n \n\n \n\n \n\n \n\n \n\n \n \n \n\n \n \n \n\n\n\n\n \n\n\n\n \n\n\n \n \n \n \n\n \n\n \n\n \n\n \n\n \n \n\n\n\n \n\n \n \n\n\n \n\n\n \n\n \n \n\n \n \n \n\n\n\n\n\n \n\n \n\n \n \n \n \n\n \n Skip to content\n\n \n \n \n \n\n\n\n\n\n \n \n\n\n\n\n\n\n \n\n \n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n \n Navigation MenuSign inAppearance settingsPlatformAI CODE CREATIONGitHub CopilotWrite better code with AIGitHub Copilot appDirect agents from issue to mergeMCP RegistryIntegrate external toolsDEVELOPER WORKFLOWSActionsAutomate any workflowCodespacesInstant dev environmentsIssuesPlan and track workCode ReviewManage code changesCode QualityEnforce quality at mergeAPPLICATION SECURITYGitHub Advanced SecurityFind and fix vulnerabilitiesCode securitySecure your code as you buildSecret protectionStop leaks before they startEXPLOREWhy GitHubDocumentationBlogChangelogMarketplaceView all featuresSolutionsBY COMPANY SIZEEnterprisesSmall and medium teamsStartupsNonprofitsBY USE CASEApp ModernizationDevSecOpsDevOpsCI/CDView all use casesBY INDUSTRYHealthcareFinancial servicesManufacturingGovernmentView all industriesView all solutionsResourcesEXPLORE BY TOPICAISoftware DevelopmentDevOpsSecurityView all topicsEXPLORE BY TYPECustomer storiesEvents & webinarsEbooks & reportsBusiness insightsGitHub SkillsSUPPORT & SERVICESDocumentationCustomer supportCommunity forumTrust centerPartnersView all resourcesOpen SourceCOMMUNITYGitHub SponsorsFund open source developersPROGRAMSSecurity LabMaintainer CommunityGitHub StarsArchive ProgramREPOSITORIESTopicsTrendingCollectionsEnterpriseENTERPRISE SOLUTIONSEnterprise platformAI-powered developer platformAVAILABLE ADD-ONSGitHub Advanced SecurityEnterprise-grade security featuresCopilot for BusinessEnterprise-grade AI featuresPremium SupportEnterprise-grade 24/7 supportPricingSearch/Sign inSign upAppearance settings\n\n\n\n\n \n \n \n \n\n You signed in with another tab or window. 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Please reload this page.\n\n\n \n \n\n\n \n\n\n\n\n\n\n\n\n \n\n \n\n \n\n \n \n \n \n \n\n \n \n \n qdrant\n \n /\n \n qdrant\n \n\n Public\n \n\n\n \n\n \n \n \n \n\n \n \n \nNotifications\n You must be signed in to change notification settings\n\n \n\n \n \n \nFork\n 2.7k\n\n \n\n \n \n \n \n\n Star\n 34.5k\n\n \n\n\n\n \n \n\n \n\n\n\n \n\n \n \n \n \n \n \n\n Code\n \n\n\n \n\n \n \n \n \n \n\n Issues\n 495\n\n\n \n\n \n \n \n \n \n\n Pull requests\n 208\n\n\n \n\n \n \n \n \n \n\n Discussions\n \n\n\n \n\n \n \n \n \n \n\n Actions\n \n\n\n \n\n \n \n \n \n \n\n Projects\n \n\n\n \n\n \n \n \n \n \n\n Security and quality\n 1\n\n\n \n\n \n \n \n \n \n\n Insights\n \n\n\n \n\n\n \n \n \n \n\nAdditional navigation options\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n Code\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Issues\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Pull requests\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Discussions\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Actions\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Projects\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Security and quality\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n Insights\n \n\n \n\n \n\n\n\n\n \n \n\n\n\n \n \n\n\n\n\n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n \n FilesExpand file treemasterBreadcrumbsqdrant/README.mdCopy pathBlameMore file actionsBlameMore file actions Latest commit HistoryHistoryHistory190 lines (123 loc) · 11.3 KBmasterBreadcrumbsqdrant/README.mdCopy pathTopFile metadata and controlsPreviewCodeBlame190 lines (123 loc) · 11.3 KBRawCopy raw fileDownload raw fileOutlineEdit and raw actions\n \n \n \n \n \n\n\n Vector Search Engine for the next generation of AI applications\n\n\n \n \n \n \n \n \n\nQdrant (read: quadrant) is a vector similarity search engine and vector database.\nIt provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.\nQdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.\nQdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.\nWith Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!\nQdrant is also available as a fully managed Qdrant Cloud ⛅ including a free tier.\n\nQuick Start • Agent Skills • Client Libraries • Demo Projects • Integrations • Contact\n\n\nGetting Started\nAgent Skills\nQdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.\nClient-Server\nTo experience the full power of Qdrant locally, run the container with this command:\ndocker run -p 6333:6333 qdrant/qdrant\nNote that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to secure your instance.\nNow you can connect to the server with any client. For example, using Python:\nfrom qdrant_client import QdrantClient\n\nclient = QdrantClient(url=\"http://localhost:6333\")\nBefore deploying Qdrant to production, be sure to read our installation and security guides.\nClients\nQdrant offers the following client libraries to help you integrate it into your application stack:\n\nOfficial:\n\nGo client\nRust client\nJavaScript/TypeScript client\nPython client\n.NET/C# client\nJava client\n\n\nCommunity:\n\nKotlin\nPHP\n\n\n\nQdrant Edge\nQdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.\nTo get started with Qdrant Edge from Python or Rust, initialize an instance of EdgeShard, which exposes methods to manage data, query it, and restore snapshots. For example:\nfrom qdrant_edge import Distance, EdgeConfig, EdgeVectorParams, EdgeShard, Point, UpdateOperation\n\nshard = EdgeShard.create(\"./shard\", EdgeConfig(\n vectors={\"my-vector\": EdgeVectorParams(size=4, distance=Distance.Cosine)}\n))\nshard.update(UpdateOperation.upsert_points([\n Point(id=1, vector={\"my-vector\": [0.1, 0.2, 0.3, 0.4]}, payload={\"color\": \"red\"})\n]))\nWhere Do I Go from Here?\n\nQuick Start Guide\nDetailed Documentation\nTake the Qdrant Essentials course\nFollow this tutorial to create a semantic search engine with Qdrant\n\nDemo Projects\nDiscover Semantic Text Search 🔍\nUnlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!\nExplore Similar Image Search - Food Discovery 🍕\nThere's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name. Check it out!\nMaster Extreme Classification - E-Commerce Product Categorization 📺\nEnter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. Play with it online!\nAPI\nREST\nQdrant provides a REST API with an OpenAPI 3.0 specification, enabling client generation for virtually any framework or programming language.\nYou can also download the raw OpenAPI definitions.\ngRPC\nFor faster, production-tier searches, Qdrant also provides a gRPC interface.\nFeatures\nDense, Sparse, and Multi Vector Search\nQdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models like ColBERT.\nFiltering on Payload\nAttach any JSON payload to your vectors and filter on it using a rich set of conditions—keyword matching, full-text, numeric ranges, geo-locations, and more—combined with should, must, and must_not clauses.\nHybrid Search\nCombine multiple vectors in a single query to get the best of semantic understanding and keyword precision, with results merged via configurable fusion strategies, such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF).\nVector Quantization and On-Disk Storage\nBuilt-in quantization cuts RAM usage by up to 97% and lets you tune the trade-off between search speed and precision.\nDistributed Deployment\nScale horizontally with sharding and replication, and update or resize collections with zero downtime.\nHighlighted Features\n\nFaceting - aggregate search results by payload values.\nRecommendation - use positive and negative examples to find similar points.\nDiscovery - constrain search to a specific region of the vector space.\nSearch Relevance Tuning - tools for adjusting search results, such as Maximal Marginal Relevance (MMR) and the Relevance Feedback Query.\nMultitenancy - scalable partitioning of data for multi-user environments.\nObservability - comprehensive metrics, telemetry, and audit logging for monitoring and debugging.\nQuery Planning and Payload Indexes - leverages stored payload information to optimize query execution strategy.\nSIMD Hardware Acceleration - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.\nGPU Support - for accelerated indexing, with support for NVIDIA and AMD GPUs.\nAsync I/O - uses io_uring to maximize disk throughput utilization even on network-attached storage.\nWrite-Ahead Logging - ensures data persistence with update confirmation, even during power outages.\n\nWeb UI\nWeb UI provides a visual way to interact with your data and monitor the health of your deployment. It enables you to explore your collections, manage data, interact with the REST API, and more.\n\nIntegrations\nQdrant integrates with the tools you're already using across every stage of your AI stack. You can connect to embedding providers, AI application frameworks, and data pipeline tools, as well as observability platforms for monitoring and tracing your vector search in production. No-code and low-code automation platforms are supported too. Refer to the Ecosystem page for the complete list.\nContributing\nWe are happy to receive your contributions! Before opening a pull request, please read our Contributing Guide.\nImportantOur development branch is dev, not master. Please fork the repo, branch from dev, and open your pull request against dev. PRs targeting master will be asked to retarget.\n\nContacts\n\nHave questions? Join our Discord channel or mention @qdrant_engine on X\nWant to stay in touch with the latest releases? Subscribe to our Newsletters\nLooking for a managed cloud? Check pricing. Need something personalized? We're at info@qdrant.tech\n\nLicense\nQdrant is licensed under the Apache License, Version 2.0. View a copy of the License file.\n \n\n\n\n\n\n \n\n\n\n \n \n\n \n\n \n Footer\n\n \n\n\n \n \n \n \n \n\n\n \n © 2026 GitHub, Inc.\n \n \n\n \n Footer navigation\n\n \n\n\n \n Terms\n \n\n \n Privacy\n \n\n\n \n Security\n \n\n \n Status\n \n\n \n Community\n \n\n \n Docs\n \n\n \n Contact\n \n\n \n\n \n \n Manage cookies\n \n \n\n\n \n \n \n Do not share my personal information\n \n \n \n\n \n \n \n\n\n\n\n \n\n\n\n\n \n \n \n\n \n \n \n\n \n You can’t perform that action at this time.","metadata":{"truncated":false},"title":"https://github.com/qdrant/qdrant/blob/master/README.md (text/html; charset=utf-8)","time":{"start":1788986628453,"end":1788986628932}},"id":"prt_g001a087e99748001JPEFtgOQv","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e97f7a0010EXFLwCm0"}} +{"type":"step_finish","timestamp":1788986628957,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087e99b46001nJM0U5d36","reason":"tool-calls","snapshot":"9a70c0f2f153f2dd1070e927e4010d2991c72850","messageID":"msg_g001a087e97f7a0010EXFLwCm0","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":74831,"input":55697,"output":62,"reasoning":0,"cache":{"write":0,"read":19072}},"cost":0}} +{"type":"step_start","timestamp":1788986678828,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ea5e2a001wOU0eTzOC","messageID":"msg_g001a087e99b91001xdHu0A7Eo","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"eb6236fc29714f4c7fcb940c89c0fe76279d3733","type":"step-start"}} +{"type":"text","timestamp":1788986768050,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ea69e8002Q5rqRAFRQ","messageID":"msg_g001a087e99b91001xdHu0A7Eo","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"Both reads succeeded — and the README surfaced a directly relevant discovery: **Qdrant Edge**, an in-process embedded variant (Rune is embedded/local-first). Evidence requirement is now well satisfied (6 sources read across official docs, official repo, primary paper, and independent analysis). Per the stage contract (\"do NOT continue into further research after the evidence requirement is satisfied\"), I'm stopping discovery/reading and writing the stage artifact.","time":{"start":1788986681832,"end":1788986768044}}} +{"type":"tool_use","timestamp":1788986768056,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"write","callID":"call_c2b18fadc0774abea31bd900","state":{"status":"completed","input":{"content":"{\n \"stage\": \"OUTWARD\",\n \"issue\": 162,\n \"round\": 1,\n \"generated_by\": \"MiMoCode headless stage dispatch\",\n \"subquestion\": {\n \"address\": \"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\"\n },\n \"required_source_classes\": [\"repo\", \"external\"],\n \"discovery\": {\n \"queries\": [\n {\n \"id\": \"Q1\",\n \"tool\": \"webfetch\",\n \"url\": \"https://duckduckgo.com/html/?q=qdrant+dense+sparse+multivector+vectors+hybrid+search+documentation\",\n \"status\": \"ok\",\n \"note\": \"DDG HTML SERP, 10 results\"\n },\n {\n \"id\": \"Q2\",\n \"tool\": \"webfetch\",\n \"url\": \"https://duckduckgo.com/html/?q=qdrant+colbert+late+interaction+reranking\",\n \"status\": \"ok\",\n \"note\": \"DDG HTML SERP, 10 results\"\n },\n {\n \"id\": \"Q3\",\n \"tool\": \"webfetch\",\n \"url\": \"https://duckduckgo.com/html/?q=qdrant+docker+local+deployment+memory+usage+resource+requirements+small+scale\",\n \"status\": \"ok\",\n \"note\": \"DDG HTML SERP, 10 results\"\n },\n {\n \"id\": \"Q4\",\n \"tool\": \"webfetch\",\n \"url\": \"https://duckduckgo.com/html/?q=colbert+late+interaction+reranking+cost+latency+tradeoffs+benchmark\",\n \"status\": \"ok\",\n \"note\": \"DDG HTML SERP, 10 results\"\n },\n {\n \"id\": \"Q5\",\n \"tool\": \"webfetch\",\n \"url\": \"https://duckduckgo.com/html/?q=sqlite-vec+vs+qdrant+embedded+vector+database+comparison\",\n \"status\": \"failed\",\n \"note\": \"DuckDuckGo bot CAPTCHA page returned; no results. Receipt preserved. Comparative embedded-vs-server query not executed; not retried.\"\n }\n ],\n \"sources_discovered\": 25,\n \"sources_selected\": 6,\n \"sources_read\": 6\n },\n \"repo_grounding\": {\n \"note\": \"Required source class 'repo' satisfied via direct code receipts from the harness repo (/mnt/workdrive/issue161-harness).\",\n \"receipts\": [\n {\n \"id\": \"R1\",\n \"path\": \"src/rune/memory_store.py\",\n \"evidence\": \"line 5 'file running sqlite-vec (dense vectors) + FTS5 (BM25) side by side'; line 10 'HYBRID RETRIEVAL: dense (vec0) + BM25 (FTS5) fused by RRF'; lines 122-124 sqlite_vec.load(db); lines 129/149 entries_fts/chunks_fts fts5 virtual tables; lines 677-679 BM25-via-FTS5 recall path; line 856 conjunctive BM25 handling\",\n \"method\": \"grep\"\n },\n {\n \"id\": \"R2\",\n \"path\": \"pyproject.toml\",\n \"evidence\": \"memory extra = [\\\"sqlite-vec>=0.1.0\\\"]; no qdrant dependency anywhere in pyproject\",\n \"method\": \"grep\"\n },\n {\n \"id\": \"R3\",\n \"path\": \"src/rune/**/*.py\",\n \"evidence\": \"case-insensitive grep for 'qdrant' across src/rune returned ZERO matches — no Qdrant code exists; the spike is a projection/comparative study, not an existing integration\",\n \"method\": \"grep\"\n },\n {\n \"id\": \"R4\",\n \"path\": \"task_tree.json INWARD context\",\n \"evidence\": \"keyword claims I1-I5 (memory_store.py, sleep_time.py, canary_p1_07.py, curator.py, rune_tui.py) carried forward; umbrella 'memory' matches 7 modules incl. memory_selector, retrieval_benchmark\",\n \"method\": \"read\"\n }\n ]\n },\n \"sources\": [\n {\n \"id\": \"O1\",\n \"url\": \"https://qdrant.tech/documentation/search/hybrid-queries/\",\n \"title\": \"Hybrid Queries - Qdrant (official docs)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_documentation\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": true,\n \"read\": true,\n \"read_receipt\": \"webfetch 200; full page captured (output truncated to tool-output file tool_g001a087e977ad001HEG21pT0W; core sections read in-context)\",\n \"discovered_via\": \"Q1\"\n },\n {\n \"id\": \"O2\",\n \"url\": \"https://qdrant.tech/documentation/search/text-search/hybrid-search/\",\n \"title\": \"Hybrid Search - Qdrant (official docs)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_documentation\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q1\",\n \"note\": \"dense+sparse via prefetch API + RRF; redundant with O1 which was read in full\"\n },\n {\n \"id\": \"O3\",\n \"url\": \"https://github.com/qdrant/qdrant\",\n \"title\": \"qdrant/qdrant - official repository README\",\n \"source_class\": \"external\",\n \"type\": \"official_repository\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": true,\n \"read\": true,\n \"read_receipt\": \"webfetch 200 x2: raw.githubusercontent.com/qdrant/qdrant/master/README.md (clean read) + github.com blob page (chrome-noisy read, same content)\",\n \"discovered_via\": \"Q1\"\n },\n {\n \"id\": \"O4\",\n \"url\": \"https://deepwiki.com/qdrant/qdrant-client/5.4-hybrid-search\",\n \"title\": \"Hybrid Search | qdrant/qdrant-client | DeepWiki\",\n \"source_class\": \"external\",\n \"type\": \"ai_generated_docs_mirror\",\n \"authority\": \"tertiary\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q1\"\n },\n {\n \"id\": \"O5\",\n \"url\": \"https://search.qdrant.tech/md/documentation/search/hybrid-queries\",\n \"title\": \"search.qdrant.tech mirror of hybrid-queries\",\n \"source_class\": \"external\",\n \"type\": \"vendor_mirror\",\n \"authority\": \"official_duplicate\",\n \"classification\": \"NOT_SELECTED_duplicate\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q1\"\n },\n {\n \"id\": \"O6\",\n \"url\": \"https://github.com/Gbrlmoraes/hybrid_search_with_qdrant\",\n \"title\": \"Hybrid Search with Qdrant (community example repo)\",\n \"source_class\": \"external\",\n \"type\": \"community_example\",\n \"authority\": \"community\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q1\"\n },\n {\n \"id\": \"O7\",\n \"url\": \"https://milos-mitrovic.online/blog/how-to-build-hybrid-search-qdrant-dense-sparse-vectors\",\n \"title\": \"How to Build Hybrid Search in Qdrant (Dense + Sparse)\",\n \"source_class\": \"external\",\n \"type\": \"community_blog\",\n \"authority\": \"community\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q1\"\n },\n {\n \"id\": \"O8\",\n \"url\": \"https://frutik.github.io/awesome-search/Articles/Hybrid-Queries---Qdrant\",\n \"title\": \"Hybrid Queries - Qdrant (awesome-search mirror)\",\n \"source_class\": \"external\",\n \"type\": \"aggregator_mirror\",\n \"authority\": \"tertiary\",\n \"classification\": \"NOT_SELECTED_duplicate\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q1\",\n \"note\": \"snippet carries the RRF weight-tuning guidance (train/val split, tune_rrf_weights helper, default 1.0/1.0 without eval set) — consistent with O1\"\n },\n {\n \"id\": \"O9\",\n \"url\": \"https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/\",\n \"title\": \"Multivectors and Late Interaction - Qdrant (official tutorial)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_documentation\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": true,\n \"read\": true,\n \"read_receipt\": \"webfetch 200; full page read in-context\",\n \"discovered_via\": \"Q2\"\n },\n {\n \"id\": \"O10\",\n \"url\": \"https://qdrant.tech/documentation/fastembed/fastembed-colbert/\",\n \"title\": \"Working with ColBERT - Qdrant (official docs, FastEmbed)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_documentation\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q2\",\n \"note\": \"FastEmbed ColBERT generation path; embedding-model side, redundant with O9's working example\"\n },\n {\n \"id\": \"O11\",\n \"url\": \"https://weaviate.io/blog/late-interaction-overview\",\n \"title\": \"An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ColQwen (Weaviate blog)\",\n \"source_class\": \"external\",\n \"type\": \"independent_vendor_analysis\",\n \"authority\": \"independent_with_caveat\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": true,\n \"read\": true,\n \"read_receipt\": \"webfetch 200; full page read in-context\",\n \"discovered_via\": \"Q4\",\n \"note\": \"written by a COMPETING vector-DB vendor — useful as vendor-neutral-ish counterweight to Qdrant docs, but not fully independent\"\n },\n {\n \"id\": \"O12\",\n \"url\": \"https://arxiv.org/abs/2004.12832\",\n \"title\": \"ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (Khattab & Zaharia, SIGIR 2020)\",\n \"source_class\": \"external\",\n \"type\": \"primary_paper\",\n \"authority\": \"primary\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": true,\n \"read\": true,\n \"read_receipt\": \"webfetch 200; abstract page read in-context (full text = PDF, not fetched)\",\n \"discovered_via\": \"Q4\"\n },\n {\n \"id\": \"O13\",\n \"url\": \"https://qdrant.github.io/fastembed/examples/ColBERT_with_FastEmbed/\",\n \"title\": \"Late Interaction Text Embedding Models (FastEmbed example)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_example\",\n \"authority\": \"official\",\n \"classification\": \"NOT_SELECTED_duplicate\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q2\"\n },\n {\n \"id\": \"O14\",\n \"url\": \"https://github.com/duksaramio/qdrant-hybrid-search-with-reranking-tutorial\",\n \"title\": \"Qdrant hybrid search with ColBERT MaxSim reranking tutorial (community)\",\n \"source_class\": \"external\",\n \"type\": \"community_example\",\n \"authority\": \"community\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q2\"\n },\n {\n \"id\": \"O15\",\n \"url\": \"https://qdrantdb.de/documentation/advanced-tutorials/reranking-hybrid-search/\",\n \"title\": \"Reranking in Hybrid Search (qdrantdb.de mirror)\",\n \"source_class\": \"external\",\n \"type\": \"unofficial_mirror\",\n \"authority\": \"tertiary\",\n \"classification\": \"NOT_SELECTED_mirror_site\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q2\"\n },\n {\n \"id\": \"O16\",\n \"url\": \"https://qdrant.tech/documentation/installation/\",\n \"title\": \"Installation - Qdrant (official docs)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_documentation\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q3\",\n \"note\": \"CPU/memory/storage/networking requirements for self-hosted deployments; not read — ops requirements partially covered by O3/O17/O18 snippets\"\n },\n {\n \"id\": \"O17\",\n \"url\": \"https://github.com/orgs/qdrant/discussions/168\",\n \"title\": \"RAM usage setup · qdrant · Discussion #168\",\n \"source_class\": \"external\",\n \"type\": \"community_discussion\",\n \"authority\": \"maintainer_answered\",\n \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q3\",\n \"note\": \"maintainer answer in snippet: no direct RAM-usage config in the service; docker --memory limits only make OOM happen earlier\"\n },\n {\n \"id\": \"O18\",\n \"url\": \"https://qdrant.tech/documentation/faq/database-optimization/\",\n \"title\": \"Database Optimization - Qdrant (official FAQ)\",\n \"source_class\": \"external\",\n \"type\": \"vendor_documentation\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q3\",\n \"note\": \"quantization + on-disk vector storage as the RAM levers; two resource-consumption scenarios\"\n },\n {\n \"id\": \"O19\",\n \"url\": \"https://markaicode.com/errors/qdrant-docker-deployment-failed-fix/\",\n \"title\": \"Fix Qdrant Docker OOMKilled (markaicode)\",\n \"source_class\": \"external\",\n \"type\": \"seo_blog\",\n \"authority\": \"low\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q3\"\n },\n {\n \"id\": \"O20\",\n \"url\": \"https://vucense.com/dev-corner/qdrant-production-setup-2026/\",\n \"title\": \"Qdrant in Production 2026: Setup & Quantization\",\n \"source_class\": \"external\",\n \"type\": \"community_blog\",\n \"authority\": \"community\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q3\",\n \"note\": \"snippet: int8 scalar quantization ~4x RAM reduction at small recall cost; ports 6333 REST / 6334 gRPC\"\n },\n {\n \"id\": \"O21\",\n \"url\": \"https://github.com/qdrant/skills/blob/main/skills/qdrant-performance-optimization/memory-usage-optimization/SKILL.md\",\n \"title\": \"qdrant/skills - memory-usage-optimization SKILL.md\",\n \"source_class\": \"external\",\n \"type\": \"official_repo_doc\",\n \"authority\": \"official\",\n \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q3\",\n \"note\": \"payload indexes + HNSW graph also require memory beyond vectors; capacity-planning guidance\"\n },\n {\n \"id\": \"O22\",\n \"url\": \"https://reranker.uk/guides/late-interaction-rerank.html\",\n \"title\": \"Late-interaction reranking — ColBERT & beyond (reranker.uk)\",\n \"source_class\": \"external\",\n \"type\": \"community_guide\",\n \"authority\": \"community\",\n \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q4\",\n \"note\": \"snippet: sub-50ms budget favors late interaction over cross-encoder; use late-interaction to prune to ~50 candidates first\"\n },\n {\n \"id\": \"O23\",\n \"url\": \"https://123ofai.com/articles/blocks/colbert-late-interaction\",\n \"title\": \"ColBERT Late Interaction in ML Systems (123ofai)\",\n \"source_class\": \"external\",\n \"type\": \"community_blog\",\n \"authority\": \"community\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q4\"\n },\n {\n \"id\": \"O24\",\n \"url\": \"https://neelmishra.github.io/blog/mlops/rag/reranking.html\",\n \"title\": \"Reranking: Cross-Encoders, ColBERT, Cohere\",\n \"source_class\": \"external\",\n \"type\": \"community_blog\",\n \"authority\": \"community\",\n \"classification\": \"NOT_SELECTED_low_authority\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"Q4\"\n },\n {\n \"id\": \"O25\",\n \"url\": \"https://arxiv.org/abs/2112.01488\",\n \"title\": \"ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction\",\n \"source_class\": \"external\",\n \"type\": \"primary_paper\",\n \"authority\": \"primary\",\n \"classification\": \"APPLICABLE_DIRECT\",\n \"selected\": false,\n \"read\": false,\n \"discovered_via\": \"O11 resources section\",\n \"note\": \"identified but not fetched — ColBERTv2 facts extracted via O11's summary; candidate for a later stage if quantization细节 matters\"\n }\n ],\n \"evidence\": [\n {\n \"id\": \"E1\",\n \"source\": \"O1\",\n \"subquestion_address\": \"dense/sparse/multivector hybrid + fusion\",\n \"claims\": [\n \"Qdrant Query API supports nested prefetch sub-requests; a query with >=1 prefetch executes each prefetch then applies the main query/fusion over their results.\",\n \"Hybrid dense+sparse search fuses via RRF or DBSF. RRF: score(d) = sum over rankings of 1/(k + rank_d); k default 2, zero-based ranks, per-prefetch weights default 1.0; k settable since v1.16.0, per-prefetch weights since v1.17.0.\",\n \"DBSF (v1.11.0+): normalizes each retriever's returned scores by 3-sigma extremes ((s-(mu-3sigma))/6sigma), then sums; stateless, computed per-query from prefetch top-k; emits 0.5 if scores degenerate; not clipped to [0,1].\",\n \"Official fusion-selection guidance: with an eval set use weighted RRF (tuned on train/val split); trusting raw score magnitudes use DBSF; otherwise RRF is the safe default. Neither RRF nor DBSF dominates universally.\",\n \"Raw-score alpha-weighted dense+sparse combination is documented as UNRELIABLE: dense (cosine, bounded) and sparse (BM25, unbounded) scores live on different per-query scales — directly mirrors Rune's memory_selector raw-score cross-scale ranking bias (P1-03 #26).\",\n \"Multi-stage pattern: dense prefetch (e.g. limit 100) then multivector (ColBERT) re-score; HNSW m=0 disables indexing for rescore-only vectors, freeing memory.\"\n ]\n },\n {\n \"id\": \"E2\",\n \"source\": \"O1\",\n \"subquestion_address\": \"recency/temporal scoring\",\n \"claims\": [\n \"Formula query (v1.14.0+) layers recency decay, popularity, geo on top of a fused RRF prefetch using $score + exp_decay over payload datetime keys — a server-side mechanism with no equivalent in Rune's SQLite stack.\"\n ]\n },\n {\n \"id\": \"E3\",\n \"source\": \"O3\",\n \"subquestion_address\": \"deployment posture / embedded feasibility\",\n \"claims\": [\n \"Qdrant is a Rust vector database, Apache-2.0 licensed, normally deployed client-server via Docker (REST :6333, gRPC :6334).\",\n \"Qdrant Edge exists: a lightweight in-process version for edge devices and resource-constrained environments — runs INSIDE the application process, stores and queries locally, syncs with a Qdrant server; Python/Rust API via EdgeShard. This is the only Qdrant mode compatible with Rune's embedded/local-first posture.\",\n \"Qdrant natively supports dense (semantic), sparse (full-text/lexical), and multivector (late-interaction models like ColBERT) vectors in one product.\",\n \"Built-in quantization 'cuts RAM usage by up to 97%'; features include WAL persistence, payload filtering, MMR, multitenancy, io_uring async I/O.\"\n ]\n },\n {\n \"id\": \"E4\",\n \"source\": \"O9\",\n \"subquestion_address\": \"multivector + ColBERT reranking mechanics\",\n \"claims\": [\n \"Qdrant supports multivectors natively: MultiVectorConfig with MultiVectorComparator.MAX_SIM; one logical document = hundreds of token-level vectors (e.g. colbert-ir/colbertv2.0 at 128 dims via FastEmbed).\",\n \"Official tutorial warns: 'most people don't use them effectively, resulting in massive RAM overhead, slow inserts, and wasted compute' — indexing every token-level vector with HNSW causes high RAM usage and slow inserts.\",\n \"Recommended pattern: keep HNSW ON for the dense vector, set hnsw_config m=0 on the ColBERT multivector (stored, not indexed) and use it only for reranking; retrieval + MaxSim rerank then happens in ONE query_points call (dense prefetch -> colbert rescore).\"\n ]\n },\n {\n \"id\": \"E5\",\n \"source\": \"O12\",\n \"subquestion_address\": \"ColBERT late-interaction foundation\",\n \"claims\": [\n \"ColBERT (SIGIR 2020, Khattab & Zaharia) independently encodes query and document with BERT, then applies a cheap fine-grained interaction (MaxSim) — document representations are precomputed offline.\",\n \"Measured vs BERT-based rerankers: two orders-of-magnitude faster and four orders-of-magnitude fewer FLOPs per query, while remaining competitive in effectiveness.\",\n \"ColBERT's pruning-friendly interaction enables end-to-end retrieval via vector-similarity indexes, not just reranking.\"\n ]\n },\n {\n \"id\": \"E6\",\n \"source\": \"O11\",\n \"subquestion_address\": \"ColBERT cost/storage trade-offs (independent view)\",\n \"claims\": [\n \"Late interaction = dual-encoder with token-level embeddings kept unpooled; MaxSim: per query token take max similarity over document tokens, then sum.\",\n \"Storage is the core disadvantage: one vector per token. Even with ColBERTv2 2-bit residual compression, a 128-dim multivector representation costs ~2-5x the storage of a single 768-1536-dim dense vector for the same documents (100-token docs; e.g. n=1000: 4.50 MB multivector-d128 vs 1.54 MB dense-d768).\",\n \"ColBERTv2 residual compression reduced ColBERTv1's MS MARCO index from 154GB to 16-25GB (6-10x) — but this compression is a ColBERTv2-index feature, and Qdrant's native multivector storage docs (O9) do not mention residual compression for stored multivectors.\",\n \"Side-benefit: token-level MaxSim scoring gives explainability (which tokens matched) analogous to keyword-match interpretability.\"\n ]\n },\n {\n \"id\": \"E7\",\n \"source\": \"O17\",\n \"subquestion_address\": \"ops footprint (small-scale server)\",\n \"claims\": [\n \"Maintainer answer (snippet): Qdrant server has NO direct max-RAM configuration; container --memory limits only trigger OOM earlier. Resource control relies on quantization/on-disk modes (O18) — relevant friction for a memory-capped single-node spike.\"\n ]\n }\n ],\n \"limitations\": [\n \"Q5 (sqlite-vec vs qdrant embedded comparison) failed on a DuckDuckGo CAPTCHA — no direct comparative evidence between Rune's current substrate (sqlite-vec) and Qdrant was collected; comparison must be derived, not cited.\",\n \"No source benchmarks Qdrant (or Qdrant Edge) at Rune's regime: single-node, sub-1M entities, embedded/local-first, many short-lived processes. All performance figures (97% RAM cut, 4x fewer FLOPs, etc.) are vendor- or paper-origin, measured on large-scale passage-search corpora, not agent-memory workloads.\",\n \"O11 is published by Weaviate, a competing vector-DB vendor — treated as independent-with-caveat; its ColBERT storage figures are derivations from the ColBERTv2 paper, not first-party benchmarks.\",\n \"O12 abstract only (PDF not fetched); quantitative claims taken from the abstract's own wording.\",\n \"Qdrant Edge maturity is unproven in the collected evidence: a single README section, no version/maturity/production-usage data, no evidence it supports sparse or multivector vectors (example shows dense only, size=4). Whether Edge supports the full dense/sparse/multivector + fusion surface is an OPEN QUESTION.\",\n \"Qdrant server (the fully-featured path) is client-server via Docker — an architectural posture mismatch with Rune's embedded SQLite stack; the spike's feasibility hinges on the Edge-vs-Server question, which the evidence does not settle.\",\n \"Snippet-level evidence (O8, O10, O16-O18, O20-O22) was classified but not read; claims from those sources are marked as snippet-derived and unverified.\"\n ],\n \"contradictions\": [\n \"C1: Qdrant markets multivector+ColBERT as a first-class native feature (O3), while its own tutorial (O9) reports that default usage produces 'massive RAM overhead, slow inserts, and wasted compute' — the feature is native but the safe configuration (m=0 rescore-only) is non-default.\",\n \"C2: ColBERTv2 residual compression achieves 6-10x storage reduction (O11/O25), but Qdrant's multivector documentation (O9) never mentions residual compression for stored multivectors — Qdrant-stored ColBERT vectors may carry raw or differently-compressed token vectors, so paper-level storage figures do not transfer to Qdrant storage.\",\n \"C3: README (O3) positions Qdrant Edge as suitable for resource-constrained/embedded use, while community/maintainer evidence (O17) shows the server product has no RAM ceiling control — the two deployment modes have opposite resource-governance stories, and Edge's capability surface is undocumented in collected sources.\"\n ],\n \"evidence_requirement_status\": \"SATISFIED: >=2 unique URLs read (6 external sources read: O1, O3, O9, O11, O12, plus O3 raw+page dual receipt) and repo class satisfied via R1-R4 code receipts. Per stage contract, no further research performed.\",\n \"stage_contract_compliance\": {\n \"real_searches_executed\": true,\n \"query_receipts_preserved\": true,\n \"source_ids_stable\": true,\n \"reads_executed\": true,\n \"read_receipts_preserved\": true,\n \"no_synthesis_performed\": true,\n \"no_code_modified\": true,\n \"no_solution_designed\": true,\n \"stopped_after_evidence_requirement\": true\n }\n}\nStageCompleted: OUTWARD\n","file_path":"/mnt/workdrive/issue161-harness/research/issue-162/outward.json"},"output":"Wrote file successfully.","metadata":{"diagnostics":{},"diff":"Index: /mnt/workdrive/issue161-harness/research/issue-162/outward.json\n===================================================================\n--- /mnt/workdrive/issue161-harness/research/issue-162/outward.json\n+++ /mnt/workdrive/issue161-harness/research/issue-162/outward.json\n@@ -0,0 +1,499 @@\n+{\n+ \"stage\": \"OUTWARD\",\n+ \"issue\": 162,\n+ \"round\": 1,\n+ \"generated_by\": \"MiMoCode headless stage dispatch\",\n+ \"subquestion\": {\n+ \"address\": \"Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)\"\n+ },\n+ \"required_source_classes\": [\"repo\", \"external\"],\n+ \"discovery\": {\n+ \"queries\": [\n+ {\n+ \"id\": \"Q1\",\n+ \"tool\": \"webfetch\",\n+ \"url\": \"https://duckduckgo.com/html/?q=qdrant+dense+sparse+multivector+vectors+hybrid+search+documentation\",\n+ \"status\": \"ok\",\n+ \"note\": \"DDG HTML SERP, 10 results\"\n+ },\n+ {\n+ \"id\": \"Q2\",\n+ \"tool\": \"webfetch\",\n+ \"url\": \"https://duckduckgo.com/html/?q=qdrant+colbert+late+interaction+reranking\",\n+ \"status\": \"ok\",\n+ \"note\": \"DDG HTML SERP, 10 results\"\n+ },\n+ {\n+ \"id\": \"Q3\",\n+ \"tool\": \"webfetch\",\n+ \"url\": \"https://duckduckgo.com/html/?q=qdrant+docker+local+deployment+memory+usage+resource+requirements+small+scale\",\n+ \"status\": \"ok\",\n+ \"note\": \"DDG HTML SERP, 10 results\"\n+ },\n+ {\n+ \"id\": \"Q4\",\n+ \"tool\": \"webfetch\",\n+ \"url\": \"https://duckduckgo.com/html/?q=colbert+late+interaction+reranking+cost+latency+tradeoffs+benchmark\",\n+ \"status\": \"ok\",\n+ \"note\": \"DDG HTML SERP, 10 results\"\n+ },\n+ {\n+ \"id\": \"Q5\",\n+ \"tool\": \"webfetch\",\n+ \"url\": \"https://duckduckgo.com/html/?q=sqlite-vec+vs+qdrant+embedded+vector+database+comparison\",\n+ \"status\": \"failed\",\n+ \"note\": \"DuckDuckGo bot CAPTCHA page returned; no results. Receipt preserved. Comparative embedded-vs-server query not executed; not retried.\"\n+ }\n+ ],\n+ \"sources_discovered\": 25,\n+ \"sources_selected\": 6,\n+ \"sources_read\": 6\n+ },\n+ \"repo_grounding\": {\n+ \"note\": \"Required source class 'repo' satisfied via direct code receipts from the harness repo (/mnt/workdrive/issue161-harness).\",\n+ \"receipts\": [\n+ {\n+ \"id\": \"R1\",\n+ \"path\": \"src/rune/memory_store.py\",\n+ \"evidence\": \"line 5 'file running sqlite-vec (dense vectors) + FTS5 (BM25) side by side'; line 10 'HYBRID RETRIEVAL: dense (vec0) + BM25 (FTS5) fused by RRF'; lines 122-124 sqlite_vec.load(db); lines 129/149 entries_fts/chunks_fts fts5 virtual tables; lines 677-679 BM25-via-FTS5 recall path; line 856 conjunctive BM25 handling\",\n+ \"method\": \"grep\"\n+ },\n+ {\n+ \"id\": \"R2\",\n+ \"path\": \"pyproject.toml\",\n+ \"evidence\": \"memory extra = [\\\"sqlite-vec>=0.1.0\\\"]; no qdrant dependency anywhere in pyproject\",\n+ \"method\": \"grep\"\n+ },\n+ {\n+ \"id\": \"R3\",\n+ \"path\": \"src/rune/**/*.py\",\n+ \"evidence\": \"case-insensitive grep for 'qdrant' across src/rune returned ZERO matches — no Qdrant code exists; the spike is a projection/comparative study, not an existing integration\",\n+ \"method\": \"grep\"\n+ },\n+ {\n+ \"id\": \"R4\",\n+ \"path\": \"task_tree.json INWARD context\",\n+ \"evidence\": \"keyword claims I1-I5 (memory_store.py, sleep_time.py, canary_p1_07.py, curator.py, rune_tui.py) carried forward; umbrella 'memory' matches 7 modules incl. memory_selector, retrieval_benchmark\",\n+ \"method\": \"read\"\n+ }\n+ ]\n+ },\n+ \"sources\": [\n+ {\n+ \"id\": \"O1\",\n+ \"url\": \"https://qdrant.tech/documentation/search/hybrid-queries/\",\n+ \"title\": \"Hybrid Queries - Qdrant (official docs)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_documentation\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": true,\n+ \"read\": true,\n+ \"read_receipt\": \"webfetch 200; full page captured (output truncated to tool-output file tool_g001a087e977ad001HEG21pT0W; core sections read in-context)\",\n+ \"discovered_via\": \"Q1\"\n+ },\n+ {\n+ \"id\": \"O2\",\n+ \"url\": \"https://qdrant.tech/documentation/search/text-search/hybrid-search/\",\n+ \"title\": \"Hybrid Search - Qdrant (official docs)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_documentation\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q1\",\n+ \"note\": \"dense+sparse via prefetch API + RRF; redundant with O1 which was read in full\"\n+ },\n+ {\n+ \"id\": \"O3\",\n+ \"url\": \"https://github.com/qdrant/qdrant\",\n+ \"title\": \"qdrant/qdrant - official repository README\",\n+ \"source_class\": \"external\",\n+ \"type\": \"official_repository\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": true,\n+ \"read\": true,\n+ \"read_receipt\": \"webfetch 200 x2: raw.githubusercontent.com/qdrant/qdrant/master/README.md (clean read) + github.com blob page (chrome-noisy read, same content)\",\n+ \"discovered_via\": \"Q1\"\n+ },\n+ {\n+ \"id\": \"O4\",\n+ \"url\": \"https://deepwiki.com/qdrant/qdrant-client/5.4-hybrid-search\",\n+ \"title\": \"Hybrid Search | qdrant/qdrant-client | DeepWiki\",\n+ \"source_class\": \"external\",\n+ \"type\": \"ai_generated_docs_mirror\",\n+ \"authority\": \"tertiary\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q1\"\n+ },\n+ {\n+ \"id\": \"O5\",\n+ \"url\": \"https://search.qdrant.tech/md/documentation/search/hybrid-queries\",\n+ \"title\": \"search.qdrant.tech mirror of hybrid-queries\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_mirror\",\n+ \"authority\": \"official_duplicate\",\n+ \"classification\": \"NOT_SELECTED_duplicate\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q1\"\n+ },\n+ {\n+ \"id\": \"O6\",\n+ \"url\": \"https://github.com/Gbrlmoraes/hybrid_search_with_qdrant\",\n+ \"title\": \"Hybrid Search with Qdrant (community example repo)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_example\",\n+ \"authority\": \"community\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q1\"\n+ },\n+ {\n+ \"id\": \"O7\",\n+ \"url\": \"https://milos-mitrovic.online/blog/how-to-build-hybrid-search-qdrant-dense-sparse-vectors\",\n+ \"title\": \"How to Build Hybrid Search in Qdrant (Dense + Sparse)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_blog\",\n+ \"authority\": \"community\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q1\"\n+ },\n+ {\n+ \"id\": \"O8\",\n+ \"url\": \"https://frutik.github.io/awesome-search/Articles/Hybrid-Queries---Qdrant\",\n+ \"title\": \"Hybrid Queries - Qdrant (awesome-search mirror)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"aggregator_mirror\",\n+ \"authority\": \"tertiary\",\n+ \"classification\": \"NOT_SELECTED_duplicate\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q1\",\n+ \"note\": \"snippet carries the RRF weight-tuning guidance (train/val split, tune_rrf_weights helper, default 1.0/1.0 without eval set) — consistent with O1\"\n+ },\n+ {\n+ \"id\": \"O9\",\n+ \"url\": \"https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/\",\n+ \"title\": \"Multivectors and Late Interaction - Qdrant (official tutorial)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_documentation\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": true,\n+ \"read\": true,\n+ \"read_receipt\": \"webfetch 200; full page read in-context\",\n+ \"discovered_via\": \"Q2\"\n+ },\n+ {\n+ \"id\": \"O10\",\n+ \"url\": \"https://qdrant.tech/documentation/fastembed/fastembed-colbert/\",\n+ \"title\": \"Working with ColBERT - Qdrant (official docs, FastEmbed)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_documentation\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q2\",\n+ \"note\": \"FastEmbed ColBERT generation path; embedding-model side, redundant with O9's working example\"\n+ },\n+ {\n+ \"id\": \"O11\",\n+ \"url\": \"https://weaviate.io/blog/late-interaction-overview\",\n+ \"title\": \"An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ColQwen (Weaviate blog)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"independent_vendor_analysis\",\n+ \"authority\": \"independent_with_caveat\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": true,\n+ \"read\": true,\n+ \"read_receipt\": \"webfetch 200; full page read in-context\",\n+ \"discovered_via\": \"Q4\",\n+ \"note\": \"written by a COMPETING vector-DB vendor — useful as vendor-neutral-ish counterweight to Qdrant docs, but not fully independent\"\n+ },\n+ {\n+ \"id\": \"O12\",\n+ \"url\": \"https://arxiv.org/abs/2004.12832\",\n+ \"title\": \"ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (Khattab & Zaharia, SIGIR 2020)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"primary_paper\",\n+ \"authority\": \"primary\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": true,\n+ \"read\": true,\n+ \"read_receipt\": \"webfetch 200; abstract page read in-context (full text = PDF, not fetched)\",\n+ \"discovered_via\": \"Q4\"\n+ },\n+ {\n+ \"id\": \"O13\",\n+ \"url\": \"https://qdrant.github.io/fastembed/examples/ColBERT_with_FastEmbed/\",\n+ \"title\": \"Late Interaction Text Embedding Models (FastEmbed example)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_example\",\n+ \"authority\": \"official\",\n+ \"classification\": \"NOT_SELECTED_duplicate\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q2\"\n+ },\n+ {\n+ \"id\": \"O14\",\n+ \"url\": \"https://github.com/duksaramio/qdrant-hybrid-search-with-reranking-tutorial\",\n+ \"title\": \"Qdrant hybrid search with ColBERT MaxSim reranking tutorial (community)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_example\",\n+ \"authority\": \"community\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q2\"\n+ },\n+ {\n+ \"id\": \"O15\",\n+ \"url\": \"https://qdrantdb.de/documentation/advanced-tutorials/reranking-hybrid-search/\",\n+ \"title\": \"Reranking in Hybrid Search (qdrantdb.de mirror)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"unofficial_mirror\",\n+ \"authority\": \"tertiary\",\n+ \"classification\": \"NOT_SELECTED_mirror_site\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q2\"\n+ },\n+ {\n+ \"id\": \"O16\",\n+ \"url\": \"https://qdrant.tech/documentation/installation/\",\n+ \"title\": \"Installation - Qdrant (official docs)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_documentation\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q3\",\n+ \"note\": \"CPU/memory/storage/networking requirements for self-hosted deployments; not read — ops requirements partially covered by O3/O17/O18 snippets\"\n+ },\n+ {\n+ \"id\": \"O17\",\n+ \"url\": \"https://github.com/orgs/qdrant/discussions/168\",\n+ \"title\": \"RAM usage setup · qdrant · Discussion #168\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_discussion\",\n+ \"authority\": \"maintainer_answered\",\n+ \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q3\",\n+ \"note\": \"maintainer answer in snippet: no direct RAM-usage config in the service; docker --memory limits only make OOM happen earlier\"\n+ },\n+ {\n+ \"id\": \"O18\",\n+ \"url\": \"https://qdrant.tech/documentation/faq/database-optimization/\",\n+ \"title\": \"Database Optimization - Qdrant (official FAQ)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"vendor_documentation\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q3\",\n+ \"note\": \"quantization + on-disk vector storage as the RAM levers; two resource-consumption scenarios\"\n+ },\n+ {\n+ \"id\": \"O19\",\n+ \"url\": \"https://markaicode.com/errors/qdrant-docker-deployment-failed-fix/\",\n+ \"title\": \"Fix Qdrant Docker OOMKilled (markaicode)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"seo_blog\",\n+ \"authority\": \"low\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q3\"\n+ },\n+ {\n+ \"id\": \"O20\",\n+ \"url\": \"https://vucense.com/dev-corner/qdrant-production-setup-2026/\",\n+ \"title\": \"Qdrant in Production 2026: Setup & Quantization\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_blog\",\n+ \"authority\": \"community\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q3\",\n+ \"note\": \"snippet: int8 scalar quantization ~4x RAM reduction at small recall cost; ports 6333 REST / 6334 gRPC\"\n+ },\n+ {\n+ \"id\": \"O21\",\n+ \"url\": \"https://github.com/qdrant/skills/blob/main/skills/qdrant-performance-optimization/memory-usage-optimization/SKILL.md\",\n+ \"title\": \"qdrant/skills - memory-usage-optimization SKILL.md\",\n+ \"source_class\": \"external\",\n+ \"type\": \"official_repo_doc\",\n+ \"authority\": \"official\",\n+ \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q3\",\n+ \"note\": \"payload indexes + HNSW graph also require memory beyond vectors; capacity-planning guidance\"\n+ },\n+ {\n+ \"id\": \"O22\",\n+ \"url\": \"https://reranker.uk/guides/late-interaction-rerank.html\",\n+ \"title\": \"Late-interaction reranking — ColBERT & beyond (reranker.uk)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_guide\",\n+ \"authority\": \"community\",\n+ \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q4\",\n+ \"note\": \"snippet: sub-50ms budget favors late interaction over cross-encoder; use late-interaction to prune to ~50 candidates first\"\n+ },\n+ {\n+ \"id\": \"O23\",\n+ \"url\": \"https://123ofai.com/articles/blocks/colbert-late-interaction\",\n+ \"title\": \"ColBERT Late Interaction in ML Systems (123ofai)\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_blog\",\n+ \"authority\": \"community\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q4\"\n+ },\n+ {\n+ \"id\": \"O24\",\n+ \"url\": \"https://neelmishra.github.io/blog/mlops/rag/reranking.html\",\n+ \"title\": \"Reranking: Cross-Encoders, ColBERT, Cohere\",\n+ \"source_class\": \"external\",\n+ \"type\": \"community_blog\",\n+ \"authority\": \"community\",\n+ \"classification\": \"NOT_SELECTED_low_authority\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"Q4\"\n+ },\n+ {\n+ \"id\": \"O25\",\n+ \"url\": \"https://arxiv.org/abs/2112.01488\",\n+ \"title\": \"ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction\",\n+ \"source_class\": \"external\",\n+ \"type\": \"primary_paper\",\n+ \"authority\": \"primary\",\n+ \"classification\": \"APPLICABLE_DIRECT\",\n+ \"selected\": false,\n+ \"read\": false,\n+ \"discovered_via\": \"O11 resources section\",\n+ \"note\": \"identified but not fetched — ColBERTv2 facts extracted via O11's summary; candidate for a later stage if quantization细节 matters\"\n+ }\n+ ],\n+ \"evidence\": [\n+ {\n+ \"id\": \"E1\",\n+ \"source\": \"O1\",\n+ \"subquestion_address\": \"dense/sparse/multivector hybrid + fusion\",\n+ \"claims\": [\n+ \"Qdrant Query API supports nested prefetch sub-requests; a query with >=1 prefetch executes each prefetch then applies the main query/fusion over their results.\",\n+ \"Hybrid dense+sparse search fuses via RRF or DBSF. RRF: score(d) = sum over rankings of 1/(k + rank_d); k default 2, zero-based ranks, per-prefetch weights default 1.0; k settable since v1.16.0, per-prefetch weights since v1.17.0.\",\n+ \"DBSF (v1.11.0+): normalizes each retriever's returned scores by 3-sigma extremes ((s-(mu-3sigma))/6sigma), then sums; stateless, computed per-query from prefetch top-k; emits 0.5 if scores degenerate; not clipped to [0,1].\",\n+ \"Official fusion-selection guidance: with an eval set use weighted RRF (tuned on train/val split); trusting raw score magnitudes use DBSF; otherwise RRF is the safe default. Neither RRF nor DBSF dominates universally.\",\n+ \"Raw-score alpha-weighted dense+sparse combination is documented as UNRELIABLE: dense (cosine, bounded) and sparse (BM25, unbounded) scores live on different per-query scales — directly mirrors Rune's memory_selector raw-score cross-scale ranking bias (P1-03 #26).\",\n+ \"Multi-stage pattern: dense prefetch (e.g. limit 100) then multivector (ColBERT) re-score; HNSW m=0 disables indexing for rescore-only vectors, freeing memory.\"\n+ ]\n+ },\n+ {\n+ \"id\": \"E2\",\n+ \"source\": \"O1\",\n+ \"subquestion_address\": \"recency/temporal scoring\",\n+ \"claims\": [\n+ \"Formula query (v1.14.0+) layers recency decay, popularity, geo on top of a fused RRF prefetch using $score + exp_decay over payload datetime keys — a server-side mechanism with no equivalent in Rune's SQLite stack.\"\n+ ]\n+ },\n+ {\n+ \"id\": \"E3\",\n+ \"source\": \"O3\",\n+ \"subquestion_address\": \"deployment posture / embedded feasibility\",\n+ \"claims\": [\n+ \"Qdrant is a Rust vector database, Apache-2.0 licensed, normally deployed client-server via Docker (REST :6333, gRPC :6334).\",\n+ \"Qdrant Edge exists: a lightweight in-process version for edge devices and resource-constrained environments — runs INSIDE the application process, stores and queries locally, syncs with a Qdrant server; Python/Rust API via EdgeShard. This is the only Qdrant mode compatible with Rune's embedded/local-first posture.\",\n+ \"Qdrant natively supports dense (semantic), sparse (full-text/lexical), and multivector (late-interaction models like ColBERT) vectors in one product.\",\n+ \"Built-in quantization 'cuts RAM usage by up to 97%'; features include WAL persistence, payload filtering, MMR, multitenancy, io_uring async I/O.\"\n+ ]\n+ },\n+ {\n+ \"id\": \"E4\",\n+ \"source\": \"O9\",\n+ \"subquestion_address\": \"multivector + ColBERT reranking mechanics\",\n+ \"claims\": [\n+ \"Qdrant supports multivectors natively: MultiVectorConfig with MultiVectorComparator.MAX_SIM; one logical document = hundreds of token-level vectors (e.g. colbert-ir/colbertv2.0 at 128 dims via FastEmbed).\",\n+ \"Official tutorial warns: 'most people don't use them effectively, resulting in massive RAM overhead, slow inserts, and wasted compute' — indexing every token-level vector with HNSW causes high RAM usage and slow inserts.\",\n+ \"Recommended pattern: keep HNSW ON for the dense vector, set hnsw_config m=0 on the ColBERT multivector (stored, not indexed) and use it only for reranking; retrieval + MaxSim rerank then happens in ONE query_points call (dense prefetch -> colbert rescore).\"\n+ ]\n+ },\n+ {\n+ \"id\": \"E5\",\n+ \"source\": \"O12\",\n+ \"subquestion_address\": \"ColBERT late-interaction foundation\",\n+ \"claims\": [\n+ \"ColBERT (SIGIR 2020, Khattab & Zaharia) independently encodes query and document with BERT, then applies a cheap fine-grained interaction (MaxSim) — document representations are precomputed offline.\",\n+ \"Measured vs BERT-based rerankers: two orders-of-magnitude faster and four orders-of-magnitude fewer FLOPs per query, while remaining competitive in effectiveness.\",\n+ \"ColBERT's pruning-friendly interaction enables end-to-end retrieval via vector-similarity indexes, not just reranking.\"\n+ ]\n+ },\n+ {\n+ \"id\": \"E6\",\n+ \"source\": \"O11\",\n+ \"subquestion_address\": \"ColBERT cost/storage trade-offs (independent view)\",\n+ \"claims\": [\n+ \"Late interaction = dual-encoder with token-level embeddings kept unpooled; MaxSim: per query token take max similarity over document tokens, then sum.\",\n+ \"Storage is the core disadvantage: one vector per token. Even with ColBERTv2 2-bit residual compression, a 128-dim multivector representation costs ~2-5x the storage of a single 768-1536-dim dense vector for the same documents (100-token docs; e.g. n=1000: 4.50 MB multivector-d128 vs 1.54 MB dense-d768).\",\n+ \"ColBERTv2 residual compression reduced ColBERTv1's MS MARCO index from 154GB to 16-25GB (6-10x) — but this compression is a ColBERTv2-index feature, and Qdrant's native multivector storage docs (O9) do not mention residual compression for stored multivectors.\",\n+ \"Side-benefit: token-level MaxSim scoring gives explainability (which tokens matched) analogous to keyword-match interpretability.\"\n+ ]\n+ },\n+ {\n+ \"id\": \"E7\",\n+ \"source\": \"O17\",\n+ \"subquestion_address\": \"ops footprint (small-scale server)\",\n+ \"claims\": [\n+ \"Maintainer answer (snippet): Qdrant server has NO direct max-RAM configuration; container --memory limits only trigger OOM earlier. Resource control relies on quantization/on-disk modes (O18) — relevant friction for a memory-capped single-node spike.\"\n+ ]\n+ }\n+ ],\n+ \"limitations\": [\n+ \"Q5 (sqlite-vec vs qdrant embedded comparison) failed on a DuckDuckGo CAPTCHA — no direct comparative evidence between Rune's current substrate (sqlite-vec) and Qdrant was collected; comparison must be derived, not cited.\",\n+ \"No source benchmarks Qdrant (or Qdrant Edge) at Rune's regime: single-node, sub-1M entities, embedded/local-first, many short-lived processes. All performance figures (97% RAM cut, 4x fewer FLOPs, etc.) are vendor- or paper-origin, measured on large-scale passage-search corpora, not agent-memory workloads.\",\n+ \"O11 is published by Weaviate, a competing vector-DB vendor — treated as independent-with-caveat; its ColBERT storage figures are derivations from the ColBERTv2 paper, not first-party benchmarks.\",\n+ \"O12 abstract only (PDF not fetched); quantitative claims taken from the abstract's own wording.\",\n+ \"Qdrant Edge maturity is unproven in the collected evidence: a single README section, no version/maturity/production-usage data, no evidence it supports sparse or multivector vectors (example shows dense only, size=4). Whether Edge supports the full dense/sparse/multivector + fusion surface is an OPEN QUESTION.\",\n+ \"Qdrant server (the fully-featured path) is client-server via Docker — an architectural posture mismatch with Rune's embedded SQLite stack; the spike's feasibility hinges on the Edge-vs-Server question, which the evidence does not settle.\",\n+ \"Snippet-level evidence (O8, O10, O16-O18, O20-O22) was classified but not read; claims from those sources are marked as snippet-derived and unverified.\"\n+ ],\n+ \"contradictions\": [\n+ \"C1: Qdrant markets multivector+ColBERT as a first-class native feature (O3), while its own tutorial (O9) reports that default usage produces 'massive RAM overhead, slow inserts, and wasted compute' — the feature is native but the safe configuration (m=0 rescore-only) is non-default.\",\n+ \"C2: ColBERTv2 residual compression achieves 6-10x storage reduction (O11/O25), but Qdrant's multivector documentation (O9) never mentions residual compression for stored multivectors — Qdrant-stored ColBERT vectors may carry raw or differently-compressed token vectors, so paper-level storage figures do not transfer to Qdrant storage.\",\n+ \"C3: README (O3) positions Qdrant Edge as suitable for resource-constrained/embedded use, while community/maintainer evidence (O17) shows the server product has no RAM ceiling control — the two deployment modes have opposite resource-governance stories, and Edge's capability surface is undocumented in collected sources.\"\n+ ],\n+ \"evidence_requirement_status\": \"SATISFIED: >=2 unique URLs read (6 external sources read: O1, O3, O9, O11, O12, plus O3 raw+page dual receipt) and repo class satisfied via R1-R4 code receipts. Per stage contract, no further research performed.\",\n+ \"stage_contract_compliance\": {\n+ \"real_searches_executed\": true,\n+ \"query_receipts_preserved\": true,\n+ \"source_ids_stable\": true,\n+ \"reads_executed\": true,\n+ \"read_receipts_preserved\": true,\n+ \"no_synthesis_performed\": true,\n+ \"no_code_modified\": true,\n+ \"no_solution_designed\": true,\n+ \"stopped_after_evidence_requirement\": true\n+ }\n+}\n+StageCompleted: OUTWARD\n","filepath":"/mnt/workdrive/issue161-harness/research/issue-162/outward.json","exists":false,"truncated":false},"title":"research/issue-162/outward.json","time":{"start":1788986768045,"end":1788986768053}},"id":"prt_g001a087ea69fa001z3oWwHX3V","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087e99b91001xdHu0A7Eo"}} +{"type":"step_finish","timestamp":1788986768086,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ebbab70014YdVXXsFM","reason":"tool-calls","snapshot":"2684feb5c5e463183f23c6aeb792e1dd2828de36","messageID":"msg_g001a087e99b91001xdHu0A7Eo","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":89097,"input":5442,"output":8839,"reasoning":0,"cache":{"write":0,"read":74816}},"cost":0}} +{"type":"step_start","timestamp":1788986771535,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ebc84d001jx6LTQng5","messageID":"msg_g001a087ebbb24001VjTdPN0ME","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"245d4ca83367c1a6c35538ef7794fb9fe677a72f","type":"step-start"}} +{"type":"tool_use","timestamp":1788986772755,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_67635801f09d4bdfaf54a5c3","state":{"status":"completed","input":{"command":"python3 -c \"import json; d=json.load(open('/mnt/workdrive/issue161-harness/research/issue-162/outward.json')); print('valid JSON;', len(d['sources']), 'sources;', len(d['evidence']), 'evidence blocks;', len(d['discovery']['queries']), 'queries')\" && tail -1 /mnt/workdrive/issue161-harness/research/issue-162/outward.json","description":"Validate artifact JSON and completion marker"},"output":"Traceback (most recent call last):\n File \"\", line 1, in \n import json; d=json.load(open('/mnt/workdrive/issue161-harness/research/issue-162/outward.json')); print('valid JSON;', len(d['sources']), 'sources;', len(d['evidence']), 'evidence blocks;', len(d['discovery']['queries']), 'queries')\n ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib/python3.14/json/__init__.py\", line 298, in load\n return loads(fp.read(),\n cls=cls, object_hook=object_hook,\n parse_float=parse_float, parse_int=parse_int,\n parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n File \"/usr/lib/python3.14/json/__init__.py\", line 352, in loads\n return _default_decoder.decode(s)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^\n File \"/usr/lib/python3.14/json/decoder.py\", line 348, in decode\n raise JSONDecodeError(\"Extra data\", s, end)\njson.decoder.JSONDecodeError: Extra data: line 499 column 1 (char 24483)\n","metadata":{"output":"Traceback (most recent call last):\n File \"\", line 1, in \n import json; d=json.load(open('/mnt/workdrive/issue161-harness/research/issue-162/outward.json')); print('valid JSON;', len(d['sources']), 'sources;', len(d['evidence']), 'evidence blocks;', len(d['discovery']['queries']), 'queries')\n ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib/python3.14/json/__init__.py\", line 298, in load\n return loads(fp.read(),\n cls=cls, object_hook=object_hook,\n parse_float=parse_float, parse_int=parse_int,\n parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n File \"/usr/lib/python3.14/json/__init__.py\", line 352, in loads\n return _default_decoder.decode(s)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^\n File \"/usr/lib/python3.14/json/decoder.py\", line 348, in decode\n raise JSONDecodeError(\"Extra data\", s, end)\njson.decoder.JSONDecodeError: Extra data: line 499 column 1 (char 24483)\n","exit":1,"description":"Validate artifact JSON and completion marker","truncated":false},"title":"Validate artifact JSON and completion marker","time":{"start":1788986772748,"end":1788986772753}},"id":"prt_g001a087ebc84e00190e3Vhndk","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087ebbb24001VjTdPN0ME"}} +{"type":"step_finish","timestamp":1788986772803,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ebcd13001sNW7wO0zJ","reason":"tool-calls","snapshot":"096baa5655f1c95f744e9700e9b9960ecf2ab0a3","messageID":"msg_g001a087ebbb24001VjTdPN0ME","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":86914,"input":6549,"output":109,"reasoning":0,"cache":{"write":0,"read":80256}},"cost":0}} +{"type":"step_start","timestamp":1788986778919,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ebe525001uoVZJgMTl","messageID":"msg_g001a087ebcd6e001JEx6oAbDO","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"3d7560014aadb9a4db9a870db5fa2b8917744686","type":"step-start"}} +{"type":"tool_use","timestamp":1788986781873,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"type":"tool","tool":"bash","callID":"call_4b4474c8961840c08fe15c74","state":{"status":"completed","input":{"command":"python3 - <<'EOF'\nimport json\nraw = open('/mnt/workdrive/issue161-harness/research/issue-162/outward.json').read()\nmarker = 'StageCompleted: OUTWARD'\nbody, _, tail = raw.rpartition(marker)\nbody = body.rstrip().rstrip('\\n')\nd = json.loads(body)\njson.dump(d, open('/mnt/workdrive/issue161-harness/research/issue-162/outward.json','w'), indent=2, ensure_ascii=False)\nopen('/mnt/workdrive/issue161-harness/research/issue-162/outward.json','a').write('\\n' + marker + '\\n')\nd2 = json.loads(open('/mnt/workdrive/issue161-harness/research/issue-162/outward.json').read().rpartition(marker)[0].rstrip())\nprint('valid;', len(d2['sources']), 'sources;', len(d2['evidence']), 'evidence blocks;', len(d2['discovery']['queries']), 'queries')\nEOF","description":"Re-serialize artifact as clean JSON + marker line"},"output":"valid; 25 sources; 7 evidence blocks; 5 queries\n","metadata":{"output":"valid; 25 sources; 7 evidence blocks; 5 queries\n","exit":0,"description":"Re-serialize artifact as clean JSON + marker line","truncated":false},"title":"Re-serialize artifact as clean JSON + marker line","time":{"start":1788986781867,"end":1788986781871}},"id":"prt_g001a087ebe527001XX38dBcS1","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","messageID":"msg_g001a087ebcd6e001JEx6oAbDO"}} +{"type":"step_finish","timestamp":1788986781899,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ebf0b0001wfWgdlLWC","reason":"tool-calls","snapshot":"8f2830ff19469e6cc0ebb24a53ec4f8074e6d662","messageID":"msg_g001a087ebcd6e001JEx6oAbDO","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":87424,"input":344,"output":232,"reasoning":0,"cache":{"write":0,"read":86848}},"cost":0}} +{"type":"step_start","timestamp":1788986789687,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ec0f35001cVVYG25wr","messageID":"msg_g001a087ebf0f5001whHxHyt90","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","snapshot":"8821e3121c9d35274d334d485c4480ff1249eae5","type":"step-start"}} +{"type":"text","timestamp":1788986798925,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ec0f40002XJQhOxF2j","messageID":"msg_g001a087ebf0f5001whHxHyt90","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"text","text":"OUTWARD stage for issue #162 is complete. Artifact: `/mnt/workdrive/issue161-harness/research/issue-162/outward.json` (validated JSON + `StageCompleted: OUTWARD`).\n\n**What actually happened**\n\n**Discovery — 5 real DDG SERP queries, receipts preserved:**\n- Q1–Q4 OK (hybrid docs, ColBERT reranking, deployment/memory footprint, ColBERT trade-offs) → ~25 candidate URLs\n- Q5 (`sqlite-vec vs qdrant embedded comparison`) **failed** on a DuckDuckGo bot CAPTCHA — recorded as a limitation, not retried\n\n**Sources — O1–O25 classified** (vendor docs, official repo, primary papers, community, mirrors). **6 read** with receipts: O1 (Qdrant Hybrid Queries docs, full), O9 (Multivectors/late-interaction tutorial, full), O3 (qdrant/qdrant README via raw.githubusercontent + page), O12 (ColBERT arXiv abstract), O11 (Weaviate late-interaction overview), plus repo grounding R1–R4.\n\n**Repo class receipts (grep-verified):** `memory_store.py` = sqlite-vec dense + FTS5 BM25 fused by RRF (lines 5, 10, 122–124); `pyproject.toml` has only `sqlite-vec>=0.1.0`; **zero** Qdrant matches in `src/rune/` — the spike is a projection study, no existing integration.\n\n**Key evidence claims (E1–E7):**\n- Qdrant Query API: nested prefetch + RRF/DBSF fusion; raw-score alpha-weighting documented as unreliable due to dense/sparse scale mismatch — directly mirrors Rune's known memory_selector raw-score ranking bias\n- ColBERT multivector is native (`MultiVectorComparator.MAX_SIM`) but official docs warn default usage causes \"massive RAM overhead, slow inserts\"; safe pattern is `hnsw m=0` rescore-only\n- ColBERT (SIGIR 2020): 100x faster, 10,000x fewer FLOPs vs BERT rerankers; independent (Weaviate) analysis: multivector storage ~2–5x dense even with ColBERTv2 compression\n- **Qdrant Edge** (in-process, embedded) discovered in the README — the only Qdrant mode compatible with Rune's local-first posture, but its maturity and feature surface (sparse/multivector support?) are an open question; the full-featured server is client-server Docker\n\n**Contradictions recorded:** native feature vs non-default safe config; ColBERTv2 compression not mentioned for Qdrant-stored multivectors; Edge embedded story vs server's no-RAM-ceiling (maintainer-confirmed).\n\nPer the stage contract: no synthesis, no design, no code changes, and research stopped once the evidence requirement (≥2 URLs read + repo receipts) was satisfied.","time":{"start":1788986789696,"end":1788986798924}}} +{"type":"step_finish","timestamp":1788986798945,"sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","part":{"id":"prt_g001a087ec334d001FB3Rlik2I","reason":"stop","snapshot":"0410afd7a0138872a228893ec0079f72688f3159","messageID":"msg_g001a087ebf0f5001whHxHyt90","sessionID":"ses_-ffe5f78183255ffeT8vvlzqHA","type":"step-finish","tokens":{"total":88076,"input":210,"output":634,"reasoning":0,"cache":{"write":0,"read":87232}},"cost":0}} diff --git a/research/issue-162/task_tree.json b/research/issue-162/task_tree.json new file mode 100644 index 00000000..c66bc04c --- /dev/null +++ b/research/issue-162/task_tree.json @@ -0,0 +1,237 @@ +{ + "issue": 162, + "round": 1, + "current_stage": "OUTWARD", + "completed_stages": [ + "ISSUE", + "INWARD" + ], + "artifacts": { + "ISSUE": { + "contract": { + "issue": 162, + "title": "Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)", + "question_type": "mixed", + "sub_questions": [ + "Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)" + ], + "required_stages": [ + "ISSUE", + "INWARD", + "OUTWARD", + "ADVERSARIAL", + "INNOVATION", + "SYNTHESIS", + "QUALIFY" + ], + "skipped_stages": { + "APPLICABILITY": "No OUTWARD research to ground against Rune." + }, + "evidence_classes": [ + "repo", + "external" + ], + "sufficient_evidence": "\u22652 code-path citations for INWARD; \u22652 unique URLs for OUTWARD (if required)", + "expected_artifact": "synthesis.json", + "ambiguous": false, + "needs_mimo": false, + "source": "deterministic" + }, + "deterministic": true + }, + "INWARD": { + "context": { + "keywords": [ + "qdrant", + "associative", + "memory", + "projection", + "spike", + "dense", + "sparse", + "multivector", + "colbert", + "late", + "interaction", + "reranking", + "plain", + "summary", + "architecture_frontier_handoff", + "highest", + "value", + "retrieval", + "experiment", + "qdrant" + ], + "umbrella_matches": [ + { + "umbrella": "memory", + "module": "memory_store", + "keyword": "memory" + }, + { + "umbrella": "memory", + "module": "memory_consolidation", + "keyword": "memory" + }, + { + "umbrella": "memory", + "module": "memory_env", + "keyword": "memory" + }, + { + "umbrella": "memory", + "module": "query_memory", + "keyword": "memory" + }, + { + "umbrella": "memory", + "module": "graph_memory", + "keyword": "memory" + }, + { + "umbrella": "memory", + "module": "memory_selector", + "keyword": "memory" + }, + { + "umbrella": "memory", + "module": "retrieval_benchmark", + "keyword": "retrieval" + } + ], + "candidate_files": [ + "src/rune/memory_store.py", + "src/rune/sleep_time.py", + "src/rune/canary_p1_07.py", + "src/rune/curator.py", + "src/rune/rune_tui.py", + "src/rune/warm_router.py", + "src/rune/abstraction.py", + "src/rune/canary_full_system.py", + "src/rune/canary_p1_01.py", + "src/rune/memory_consolidation.py", + "src/rune/rune_operator.py", + "src/rune/cache.py", + "src/rune/canary_p1_02.py", + "src/rune/canary_p1_06.py", + "src/rune/canary_tui_drift.py" + ], + "candidate_tests": [ + "tests/test_p1_03_memory_selection.py", + "tests/test_p1_07_runtime_objects.py", + "tests/test_retrieval_benchmark.py", + "tests/test_p0_07_inference_trace.py", + "tests/test_p1_01_capability_authority.py", + "tests/test_synthesis_executor.py", + "tests/test_context_broker.py", + "tests/test_failure_auto_filer.py", + "tests/test_p1_02_capsule_authority.py", + "tests/test_p1_06_model_routing.py" + ], + "define_contract": { + "issue": 162, + "title": "Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)", + "question_type": "mixed", + "sub_questions": [ + "Address: Qdrant associative-memory projection spike (dense/sparse/multivector + ColBERT late-interaction reranking)" + ], + "required_stages": [ + "ISSUE", + "INWARD", + "OUTWARD", + "ADVERSARIAL", + "INNOVATION", + "SYNTHESIS", + "QUALIFY" + ], + "skipped_stages": { + "APPLICABILITY": "No OUTWARD research to ground against Rune." + }, + "evidence_classes": [ + "repo", + "external" + ], + "sufficient_evidence": "\u22652 code-path citations for INWARD; \u22652 unique URLs for OUTWARD (if required)", + "expected_artifact": "synthesis.json", + "ambiguous": false, + "needs_mimo": false, + "source": "deterministic" + }, + "mimo_needed": false + }, + "deterministic": true, + "claims": [ + { + "id": "I1", + "claim": "Relevant file: src/rune/memory_store.py", + "evidence": [ + "src/rune/memory_store.py" + ], + "status": "CURRENT" + }, + { + "id": "I2", + "claim": "Relevant file: src/rune/sleep_time.py", + "evidence": [ + "src/rune/sleep_time.py" + ], + "status": "CURRENT" + }, + { + "id": "I3", + "claim": "Relevant file: src/rune/canary_p1_07.py", + "evidence": [ + "src/rune/canary_p1_07.py" + ], + "status": "CURRENT" + }, + { + "id": "I4", + "claim": "Relevant file: src/rune/curator.py", + "evidence": [ + "src/rune/curator.py" + ], + "status": "CURRENT" + }, + { + "id": "I5", + "claim": "Relevant file: src/rune/rune_tui.py", + "evidence": [ + "src/rune/rune_tui.py" + ], + "status": "CURRENT" + } + ] + } + }, + "blocking_unknowns": [], + "status": "ACTIVE", + "stage_history": [ + { + "stage": "ISSUE", + "round": 1, + "completed_at": "auto", + "artifact_keys": [ + "contract", + "deterministic" + ] + }, + { + "stage": "INWARD", + "round": 1, + "completed_at": "auto", + "artifact_keys": [ + "context", + "deterministic", + "claims" + ] + }, + { + "stage": "OUTWARD", + "round": 1, + "failed": true, + "reason": "OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \u2014 search snippets alone do not count as evidence" + } + ] +} \ No newline at end of file diff --git a/research/issue-162/telemetry/attempts.jsonl b/research/issue-162/telemetry/attempts.jsonl new file mode 100644 index 00000000..008f8b6b --- /dev/null +++ b/research/issue-162/telemetry/attempts.jsonl @@ -0,0 +1,3 @@ +{"attempt_id": "issue-162-ISSUE-1-a1", "event_id": "issue-162-ISSUE-1", "stage": "ISSUE", "issue": 162, "process_id": "research-162-r1", "started_at": "2026-09-09T20:41:27.955220+00:00", "finished_at": "2026-09-09T20:41:28.148457+00:00", "terminal_status": "COMPLETED", "wall_time_s": 0.1933, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-162-INWARD-1-a1", "event_id": "issue-162-INWARD-1", "stage": "INWARD", "issue": 162, "process_id": "research-162-r1", "started_at": "2026-09-09T20:41:28.149060+00:00", "finished_at": "2026-09-09T20:41:28.297378+00:00", "terminal_status": "COMPLETED", "wall_time_s": 0.1484, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-162-OUTWARD-1-a1", "event_id": "issue-162-OUTWARD-1", "stage": "OUTWARD", "issue": 162, "process_id": "research-162-r1", "started_at": "2026-09-09T20:41:49.531028+00:00", "finished_at": "2026-09-09T20:48:41.608706+00:00", "terminal_status": "GATE_FAILED", "wall_time_s": 412.0777, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} diff --git a/research/issue-310/.consumed_issue-310-INWARD-1 b/research/issue-310/.consumed_issue-310-INWARD-1 new file mode 100644 index 00000000..e2b53679 --- /dev/null +++ b/research/issue-310/.consumed_issue-310-INWARD-1 @@ -0,0 +1 @@ +consumed at 1788984934.791921 \ No newline at end of file diff --git a/research/issue-310/.consumed_issue-310-ISSUE-1 b/research/issue-310/.consumed_issue-310-ISSUE-1 new file mode 100644 index 00000000..94c62e01 --- /dev/null +++ b/research/issue-310/.consumed_issue-310-ISSUE-1 @@ -0,0 +1 @@ +consumed at 1788984909.948637 \ No newline at end of file diff --git a/research/issue-310/.consumed_issue-310-OUTWARD-1 b/research/issue-310/.consumed_issue-310-OUTWARD-1 new file mode 100644 index 00000000..98827d47 --- /dev/null +++ b/research/issue-310/.consumed_issue-310-OUTWARD-1 @@ -0,0 +1 @@ +consumed at 1788984961.9986615 \ No newline at end of file diff --git a/research/issue-310/.consumed_issue-310-OUTWARD-2 b/research/issue-310/.consumed_issue-310-OUTWARD-2 new file mode 100644 index 00000000..a74997cc --- /dev/null +++ b/research/issue-310/.consumed_issue-310-OUTWARD-2 @@ -0,0 +1 @@ +consumed at 1788985330.2829049 \ No newline at end of file diff --git a/research/issue-310/outward.json b/research/issue-310/outward.json new file mode 100644 index 00000000..4c072534 --- /dev/null +++ b/research/issue-310/outward.json @@ -0,0 +1,151 @@ +{ + "stage": "OUTWARD", + "issue": 310, + "round": 1, + "subquestion": "ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path — model_time/tool_time always 0", + "required_source_classes": ["repo"], + "queries": [ + { + "kind": "QUERY", + "tool": "Grep", + "pattern": "model_time|tool_time", + "scope": "whole repo", + "hits": 51, + "receipt": "matches in research/issue-310/task_tree.json, telemetry/attempts.jsonl (3 attempt records), outward.json (prior attempt), scripts/stage_telemetry.py:13,140,141,155,156, src/rune/rune_operator.py:1574, src/rune/inference_trace.py:69, docs/inference-trace.md:35, plans/2026-09-05-p0-07-inference-trace.md:78,120,158, canary_p0_07.py, and 5 test files (test_p0_07_inference_trace.py, test_p1_01/02/03/06)" + }, + { + "kind": "QUERY", + "tool": "Grep", + "pattern": "mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start", + "scope": "whole repo", + "hits": 16, + "receipt": "ALL matches confined to: scripts/stage_telemetry.py (docstring lines 24-25 + definitions 83-115), tests/test_stage_telemetry.py:62-63,77 (invocations), and research/issue-310 artifacts (prior report + events log). ZERO matches in src/ and ZERO in scripts/ outside stage_telemetry.py itself." + }, + { + "kind": "QUERY", + "tool": "Grep", + "pattern": "StageAttempt|stage_telemetry", + "scope": "whole repo", + "hits": 47, + "receipt": "Production importers: scripts/research_stage_executor.py:87-91 ONLY. Tests: tests/test_stage_telemetry.py. src/: zero matches (src/rune has no StageAttempt reference)." + }, + { + "kind": "QUERY", + "tool": "Grep", + "pattern": "ResearchEventRelay|elapsed", + "include": "scripts/research_stage_executor.py", + "hits": 20, + "receipt": "5 relay invocations (lines 363-364, 585-586, 755-756, 917-918, 1072-1073, 1182-1183), each followed by relay_result['elapsed'] extraction (lines 501, 658, 811, 964, 1243, 1306, 1407); line 1215 shows a no-op relay_result with elapsed 0.0. No StageAttempt/telemetry references anywhere in the executor's stage bodies." + }, + { + "kind": "SOURCE_READ", + "tool": "Read", + "target": "scripts/stage_telemetry.py", + "lines": "1-221 (full)" + }, + { + "kind": "SOURCE_READ", + "tool": "Read", + "target": "scripts/research_stage_executor.py", + "lines": "60-129" + }, + { + "kind": "SOURCE_READ", + "tool": "Read", + "target": "tests/test_stage_telemetry.py", + "lines": "50-109" + } + ], + "sources": [ + { + "id": "O1", + "path": "scripts/stage_telemetry.py", + "classification": "PRIMARY", + "locus": "lines 2-28 (module docstring, OBS-1 epic #284/#287), 45-79 (StageAttempt.__init__: window lists + counters), 83-115 (mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end), 119-128 (finish() force-closes unclosed windows), 132-143 (summary(): model_time_s/tool_time_s = summed window pairs, explicitly non-additive), 186-218 (TelemetryLedger append-only persist, never raises)" + }, + { + "id": "O2", + "path": "scripts/research_stage_executor.py", + "classification": "PRIMARY", + "locus": "lines 85-101: sole production importer; constructs StageAttempt + TelemetryLedger per stage event, runs _execute_stage, then calls finish(status-mapping) + persist. NO mark_* call between construction (line 89) and finish (line 96)." + }, + { + "id": "O3", + "path": "tests/test_stage_telemetry.py", + "classification": "TEST_ONLY", + "locus": "lines 59-71 (only invocation site of mark_model_start/end + mark_tool_start/end in the entire repo), 74-81 (unclosed-window interrupt path)" + }, + { + "id": "O4", + "path": "research/issue-310/telemetry/attempts.jsonl", + "classification": "EMPIRICAL", + "locus": "lines 1-3: real persisted production attempt records. ISSUE stage: wall 0.1597s, model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, window_counts all 0. INWARD stage: wall 0.2246s, same zeros. Prior OUTWARD attempt (GATE_FAILED, wall 315.06s): model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0 despite 315 seconds of real model+tool work — the strongest single datum for B1." + }, + { + "id": "O5", + "path": "src/rune/inference_trace.py + src/rune/rune_operator.py", + "classification": "DISTINCT-SCHEMA", + "locus": "inference_trace.py:69 (InferenceTrace.model_time_ms field), rune_operator.py:1574 (model_time_ms=round(run_usage['duration_ns']/1e6, 3)) — a working, separately-persisted model-timing surface (Ollama total_duration via transport accumulators), NOT StageAttempt.model_time_s" + }, + { + "id": "O6", + "path": "scripts/research_stage_executor.py (stage bodies)", + "classification": "ADJACENT", + "locus": "lines 363-364, 585-586, 755-756, 917-918, 1072-1073: every stage body runs the mimo worker subprocess via ResearchEventRelay and extracts only relay_result['elapsed'] (coarse wall-clock). ResearchEventRelay has no StageAttempt references — the executor/relay boundary is exactly where model/tool time accrues without being marked." + } + ], + "evidence": [ + { + "id": "E1", + "source": "O1", + "claim": "StageAttempt defines four window-marker pairs (model, tool, gate, persistence) plus counters (model_calls, tool_calls, sources_discovered, ...); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish(); categories are explicitly non-additive vs wall_time.", + "locus": "scripts/stage_telemetry.py:59-79, 83-128, 132-143, 154-163" + }, + { + "id": "E2", + "source": "O2", + "claim": "The stage executor is the ONLY production code importing stage_telemetry; it wraps every stage with StageAttempt(...)/finish(...)/ledger.persist(...) but NEVER calls any mark_* window marker or increments any counter between construction and finish — model_time_s/tool_time_s/window_counts are therefore structurally always 0 in production.", + "locus": "scripts/research_stage_executor.py:87-101" + }, + { + "id": "E3", + "source": "O3", + "claim": "mark_model_start/mark_model_end/mark_tool_start/mark_tool_end are invoked only in tests/test_stage_telemetry.py:62-63,77; grep over the whole repo finds zero mark_* call sites in src/ and zero in scripts/ outside stage_telemetry.py's own definitions/docstring.", + "locus": "tests/test_stage_telemetry.py:62-63, 77" + }, + { + "id": "E4", + "source": "O4", + "claim": "Empirical confirmation in this round's own production records: all three persisted attempts (ISSUE 0.1597s, INWARD 0.2246s, OUTWARD 315.06s wall) show model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, window_counts all 0 — including the OUTWARD attempt that consumed 315 seconds of real model/tool work.", + "locus": "research/issue-310/telemetry/attempts.jsonl:1-3" + }, + { + "id": "E5", + "source": "O6", + "claim": "The stage bodies' actual model invocation happens inside mimo worker subprocesses driven by ResearchEventRelay, which records only a coarse wall-clock 'elapsed'; relay results are folded into stage results but never into StageAttempt windows — the instrumentation gap is precisely at the executor/relay boundary.", + "locus": "scripts/research_stage_executor.py:363-364/501, 585-586/658, 755-756/811, 917-918/964, 1072-1073/1243" + }, + { + "id": "E6", + "source": "O5", + "claim": "A similarly-named but separate timing surface exists and DOES function: InferenceTrace.model_time_ms is populated from Ollama transport usage_total_duration_ns in the operator path. This is NOT StageAttempt.model_time_s; conflation risk when reading ADAPT-1 claims about 'model_time'.", + "locus": "src/rune/inference_trace.py:69; src/rune/rune_operator.py:1574; docs/inference-trace.md:35" + }, + { + "id": "E7", + "source": "O1", + "claim": "stage_telemetry.py self-documents the design constraint: observational only, every attempt persists its own record including failures, categories observed and non-additive. The executor's finish() status mapping (lines 96-100) covers COMPLETED/GATE_FAILED/FAILED/INTERRUPTED; TIMEOUT is defined in TERMINAL_STATUSES but not visible in the executor's mapping expression (out of B1 scope, noted as open).", + "locus": "scripts/stage_telemetry.py:2-28, 37; scripts/research_stage_executor.py:96-100" + } + ], + "limitations": [ + "Grep receipts cover all file types repo-wide for the mark_* pattern (16 hits, no include filter), so the 'test-only invocation' finding is not limited to *.py.", + "Whether ResearchEventRelay itself or the mimo subprocess could emit window events that the executor drops was not traced into research_event_relay.py internals; however, grep for StageAttempt/mark_* shows the relay module never references telemetry, so no hidden call sites exist there.", + "TIMEOUT terminal-status handling: defined in stage_telemetry.py TERMINAL_STATUSES and force-closed at finish(), but the executor's status mapping expression does not map TIMEOUT; whether any caller can currently produce TIMEOUT was not traced (out of B1 scope).", + "The prior OUTWARD attempt's artifact (outward.json, GATE_FAILED) was overwritten by this refresh; prior attempt's query receipts are superseded by this run's, which were re-executed for real." + ], + "contradictions": [ + "None found on B1 itself: static evidence (zero production call sites for mark_* markers, O2/O3) and empirical evidence (attempts.jsonl all zeros including a 315s attempt, O4) agree.", + "Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) — same 'model_time' naming family, different pipelines; any source claiming 'model_time is populated' would be true of the former and false of the latter." + ] +} diff --git a/research/issue-310/stage_OUTWARD.events.jsonl b/research/issue-310/stage_OUTWARD.events.jsonl new file mode 100644 index 00000000..8a52edc6 --- /dev/null +++ b/research/issue-310/stage_OUTWARD.events.jsonl @@ -0,0 +1,29 @@ +{"type":"step_start","timestamp":1788985346754,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d60abf001bmTR46fhY","messageID":"msg_g001a087d5d05a001jO6g0Aq9i","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"718a4db6c63e87eeec5efeb65793abe4746e39be","type":"step-start"}} +{"type":"text","timestamp":1788985348450,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d60cd3001DeIBXXXUf","messageID":"msg_g001a087d5d05a001jO6g0Aq9i","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"text","text":"I'll first check for a matching skill, then run the repo discovery for issue #310's telemetry-marker subquestion.","time":{"start":1788985347283,"end":1788985348446}}} +{"type":"tool_use","timestamp":1788985348454,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"skill_search","callID":"call_3d6b1ffcb4f74ba18db8cd16","state":{"status":"completed","input":{"query":"Research stage outward: discover, classify, and read repo sources as evidence for a subquestion about telemetry markers never invoked in production path; preserve query/source receipts; no code modification. Input: local repository + issue #310 context. Output: evidence report with stable source IDs (O1, O2...) and receipts. Audience: research orchestrator."},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"firecrawl-search\",\n \"name\": \"firecrawl-search\",\n \"score\": 0.5986,\n \"reason\": \"The skill description matches these query terms: research, discover, source, about, in.\"\n },\n {\n \"skill_id\": \"sales\",\n \"name\": \"sales\",\n \"score\": 0.58,\n \"reason\": \"The skill description matches these query terms: research, evidence, invoked, context.\"\n },\n {\n \"skill_id\": \"deep-research\",\n \"name\": \"deep-research\",\n \"score\": 0.5868,\n \"reason\": \"The skill description matches these query terms: research, source, in, no, report.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788985348448,"end":1788985348451}},"id":"prt_g001a087d60cd9001UWTg9AbsF","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d5d05a001jO6g0Aq9i"}} +{"type":"step_finish","timestamp":1788985348556,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d61165001OAf5LV9A0","reason":"tool-calls","snapshot":"e7b2b0b9e2ece4c313227411877c11f10584f85a","messageID":"msg_g001a087d5d05a001jO6g0Aq9i","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":28884,"input":28476,"output":408,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}} +{"type":"step_start","timestamp":1788985362209,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d6471e0010pXeTPmXS","messageID":"msg_g001a087d61247001eDqV40rEk","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"c91e22924437b076977e941f13f15bcb4df51428","type":"step-start"}} +{"type":"text","timestamp":1788985362742,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d647e7002HQC2UcHPk","messageID":"msg_g001a087d61247001eDqV40rEk","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"text","text":"No strong skill match (local-repo evidence task) — proceeding with direct repo discovery. First, locating the working repo and searching for the telemetry markers.","time":{"start":1788985362407,"end":1788985362739}}} +{"type":"tool_use","timestamp":1788985362928,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"grep","callID":"call_8092e5f1bc0e42a2a9898330","state":{"status":"completed","input":{"pattern":"model_time|tool_time"},"output":"Found 51 matches\n/mnt/workdrive/issue161-harness/research/issue-310/task_tree.json:\n Line 13: \"title\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\",\n\n Line 16: \"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\"\n\n Line 53: \"model_time\",\n\n Line 54: \"tool_time\",\n\n Line 133: \"title\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\",\n\n Line 136: \"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\"\n\n\n/mnt/workdrive/issue161-harness/research/issue-310/telemetry/attempts.jsonl:\n Line 1: {\"attempt_id\": \"issue-310-ISSUE-1-a1\", \"event_id\": \"issue-310-ISSUE-1\", \"stage\": \"ISSUE\", \"issue\": 310, \"process_id\": \"research-310-r1\", \"started_at\": \"2026-09-09T20:15:09.949030+00:00\", \"finished_at\": \"2026-09-09T20:15:10.108621+00:00\", \"terminal_status\": \"COMPLETED\", \"wall_time_s\": 0.1597, \"model_time_s\": 0, \"tool_time_s\": 0, \"gate_time_s\": 0, \"persistence_time_s\": 0, \"overlap_note\": \"categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive\", \"model_calls\": 0, \"tool_calls\": 0, \"repo_read_calls\": 0, \"external_queries\": 0, \"sources_discovered\": 0, \"sources_read\": 0, \"retries\": 0, \"reconciliation_actions\": 0, \"implementation_corrections\": 0, \"ci_failures\": 0, \"input_tokens\": \"UNKNOWN_NOT_EXPOSED\", \"output_tokens\": \"UNKNOWN_NOT_EXPOSED\", \"queue_wait_s\": null, \"window_counts\": {\"model\": 0, \"tool\": 0, \"gate\": 0, \"persistence\": 0}}\n\n Line 2: {\"attempt_id\": \"issue-310-INWARD-1-a1\", \"event_id\": \"issue-310-INWARD-1\", \"stage\": \"INWARD\", \"issue\": 310, \"process_id\": \"research-310-r1\", \"started_at\": \"2026-09-09T20:15:34.792311+00:00\", \"finished_at\": \"2026-09-09T20:15:35.016831+00:00\", \"terminal_status\": \"COMPLETED\", \"wall_time_s\": 0.2246, \"model_time_s\": 0, \"tool_time_s\": 0, \"gate_time_s\": 0, \"persistence_time_s\": 0, \"overlap_note\": \"categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive\", \"model_calls\": 0, \"tool_calls\": 0, \"repo_read_calls\": 0, \"external_queries\": 0, \"sources_discovered\": 0, \"sources_read\": 0, \"retries\": 0, \"reconciliation_actions\": 0, \"implementation_corrections\": 0, \"ci_failures\": 0, \"input_tokens\": \"UNKNOWN_NOT_EXPOSED\", \"output_tokens\": \"UNKNOWN_NOT_EXPOSED\", \"queue_wait_s\": null, \"window_counts\": {\"model\": 0, \"tool\": 0, \"gate\": 0, \"persistence\": 0}}\n\n Line 3: {\"attempt_id\": \"issue-310-OUTWARD-1-a1\", \"event_id\": \"issue-310-OUTWARD-1\", \"stage\": \"OUTWARD\", \"issue\": 310, \"process_id\": \"research-310-r1\", \"started_at\": \"2026-09-09T20:16:01.999099+00:00\", \"finished_at\": \"2026-09-09T20:21:17.061293+00:00\", \"terminal_status\": \"GATE_FAILED\", \"wall_time_s\": 315.0622, \"model_time_s\": 0, \"tool_time_s\": 0, \"gate_time_s\": 0, \"persistence_time_s\": 0, \"overlap_note\": \"categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive\", \"model_calls\": 0, \"tool_calls\": 0, \"repo_read_calls\": 0, \"external_queries\": 0, \"sources_discovered\": 0, \"sources_read\": 0, \"retries\": 0, \"reconciliation_actions\": 0, \"implementation_corrections\": 0, \"ci_failures\": 0, \"input_tokens\": \"UNKNOWN_NOT_EXPOSED\", \"output_tokens\": \"UNKNOWN_NOT_EXPOSED\", \"queue_wait_s\": null, \"window_counts\": {\"model\": 0, \"tool\": 0, \"gate\": 0, \"persistence\": 0}}\n\n\n/mnt/workdrive/issue161-harness/research/issue-310/outward.json:\n Line 6: \"subquestion\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path — model_time/tool_time always 0\",\n\n Line 11: \"query\": \"rg 'model_time|tool_time' src/\",\n\n Line 43: \"receipt\": \"both records show model_time_s=0, tool_time_s=0, gate_time_s=0, persistence_time_s=0, model_calls=0, tool_calls=0, window_counts all 0, wall_time_s 0.1597/0.2246\"\n\n Line 58: \"classification\": \"PRIMARY — the StageAttempt telemetry module (OBS-1, epic #284/#287); defines mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end, summary() deriving model_time_s/tool_time_s from summed windows\"\n\n Line 86: \"classification\": \"DISTINCT-SCHEMA — InferenceTrace.model_time_ms (line 69) is a different telemetry line (Ollama total_duration via transport accumulators), NOT the StageAttempt model_time_s; named similarly, easily conflated\"\n\n Line 93: \"classification\": \"DISTINCT-SCHEMA — writes InferenceTrace.model_time_ms = run_usage duration_ns / 1e6 (line 1574); this is the model-side timing that DOES work in Rune's operator path, unlike StageAttempt's model_time_s\"\n\n Line 100: \"classification\": \"EMPIRICAL — real production attempt records for issue #310 showing model_time_s=0, tool_time_s=0 across ISSUE and INWARD stages\"\n\n Line 119: \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish().\",\n\n Line 137: \"claim\": \"Empirical confirmation in this very round's production records: both persisted attempts (ISSUE 0.1597s wall, INWARD 0.2246s wall) show model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, and all window_counts 0 — the markers were never invoked.\",\n\n Line 155: \"claim\": \"A similarly-named but separate timing surface exists and DOES function: InferenceTrace.model_time_ms is populated from Ollama transport usage_total_duration_ns in the operator path (src/rune/rune_operator.py:1574). This is NOT StageAttempt.model_time_s; conflation risk when reading ADAPT-1 claims.\",\n\n Line 179: \"None found on B1 itself: static evidence (no production call sites for mark_* markers) and empirical evidence (real attempts.jsonl records with model_time_s=0/tool_time_s=0) agree.\",\n\n Line 180: \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (works, populated from transport accumulators) vs StageAttempt.model_time_s (always 0) — same 'model_time' family, different pipelines; a source citing 'model_time is populated' would be true of the former and false of the latter.\"\n\n Line 183: {\"kind\": \"QUERY\", \"tool\": \"memory search\", \"query\": \"ADAPT-1 telemetry model_time tool_time issue 310\", \"result\": \"10 BM25 hits; cross-project context on OBS-1/#287/#282\"},\n\n Line 184: {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"model_time|tool_time\", \"scope\": \"src/\", \"hits\": 6},\n\n Line 186: {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"model_time|tool_time\", \"scope\": \"repo *.py\", \"hits\": 22},\n\n\n/mnt/workdrive/issue161-harness/scripts/stage_telemetry.py:\n Line 13: model_time + tool_time when MiMo waits on a tool or relay overhead overlaps.\n\n Line 140: model_time = self._sum_windows(self.model_windows)\n\n Line 141: tool_time = self._sum_windows(self.tool_windows)\n\n Line 155: \"model_time_s\": model_time,\n\n Line 156: \"tool_time_s\": tool_time,\n\n\n/mnt/workdrive/issue161-harness/tests/test_p1_03_memory_selection.py:\n Line 1025: model_time_ms=0.0,\n\n\n/mnt/workdrive/issue161-harness/tests/test_p1_01_capability_authority.py:\n Line 345: model_time_ms=0.0,\n\n\n/mnt/workdrive/issue161-harness/tests/test_p1_06_model_routing.py:\n Line 317: \"model_time_ms\": 1.0,\n\n Line 351: model_time_ms=1.0,\n\n\n/mnt/workdrive/issue161-harness/tests/test_p1_02_capsule_authority.py:\n Line 398: model_time_ms=0.0,\n\n Line 432: \"model_time_ms\": 0.0,\n\n\n/mnt/workdrive/issue161-harness/src/rune/rune_operator.py:\n Line 1574: model_time_ms=round(run_usage[\"duration_ns\"] / 1e6, 3),\n\n\n/mnt/workdrive/issue161-harness/src/rune/inference_trace.py:\n Line 69: model_time_ms: float # sum total_duration / 1e6\n\n\n/mnt/workdrive/issue161-harness/docs/inference-trace.md:\n Line 35: | `model_time_ms` | transport `usage_total_duration_ns` / 1e6 | Ollama `total_duration` sum |\n\n\n/mnt/workdrive/issue161-harness/tests/test_p0_07_inference_trace.py:\n Line 119: model_time_ms=round(getattr(transport, \"usage_total_duration_ns\", 0) / 1e6, 3),\n\n Line 207: \"model_time_ms\",\n\n Line 247: assert record[\"model_time_ms\"] == 7000.0\n\n Line 301: \"\"\"latency_ms (wall) and model_time_ms (Ollama total_duration sum) +\n\n Line 315: assert record[\"model_time_ms\"] == 4000.0\n\n\n/mnt/workdrive/issue161-harness/src/rune/canary_p0_07.py:\n Line 57: \"model_time_ms\",\n\n Line 162: model_time_ms=round(getattr(transport, \"usage_total_duration_ns\", 0) / 1e6, 3),\n\n Line 195: \"model_time_ms\": float(defaults[\"model_time_ms\"]),\n\n Line 224: if record[\"output_tokens\"] != 84 or record[\"model_time_ms\"] != 2000.0:\n\n\n/mnt/workdrive/issue161-harness/research/full-audit/group-a.md:\n Line 21: - [MEDIUM] **`_get_model_timeout` defined but never called** (lines 2260–2287). Dead code — the method exists but no code path invokes it. The actual timeout logic lives in `rune_operator._execute_on_model` with different values.\n\n\n/mnt/workdrive/issue161-harness/plans/2026-09-05-p0-07-inference-trace.md:\n Line 78: model_time_ms: float # sum total_duration / 1e6\n\n Line 120: \"\"\"latency_ms (wall) and model_time_ms (Ollama total_duration sum) +\n\n Line 158: model_time_ms=round(getattr(transport, \"usage_total_duration_ns\", 0) / 1e6, 3),\n","metadata":{"matches":51,"truncated":false},"title":"model_time|tool_time","time":{"start":1788985362742,"end":1788985362926}},"id":"prt_g001a087d6483c001tM1v0iSWG","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d61247001eDqV40rEk"}} +{"type":"tool_use","timestamp":1788985363017,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"bash","callID":"call_2c28bd85b84d49e4931ff2d5","state":{"status":"completed","input":{"command":"pwd && ls","description":"Show current working directory contents"},"output":"/mnt/workdrive/issue161-harness\nAGENTS.md\nalternatives.json\nbackups\nconfig\ndocs\nhenxels_checks.py\nhenxels.yaml\nlint-baseline.json\nmemory\nplans\nPRD.md\nprompts\n__pycache__\npyproject.toml\npyright-baseline.json\npyrightconfig.json\nREADME.md\nreports\nrequirements-ci.txt\nresearch\nscripts\nshared\nspecs\nsrc\nsurvey\ntests\nwal-risks.json\n","metadata":{"output":"/mnt/workdrive/issue161-harness\nAGENTS.md\nalternatives.json\nbackups\nconfig\ndocs\nhenxels_checks.py\nhenxels.yaml\nlint-baseline.json\nmemory\nplans\nPRD.md\nprompts\n__pycache__\npyproject.toml\npyright-baseline.json\npyrightconfig.json\nREADME.md\nreports\nrequirements-ci.txt\nresearch\nscripts\nshared\nspecs\nsrc\nsurvey\ntests\nwal-risks.json\n","exit":0,"description":"Show current working directory contents","truncated":false},"title":"Show current working directory contents","time":{"start":1788985363014,"end":1788985363016}},"id":"prt_g001a087d647ed001GRmMBDVVA","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d61247001eDqV40rEk"}} +{"type":"step_finish","timestamp":1788985363254,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d64a490014oswwE8fD","reason":"tool-calls","snapshot":"21020e34a7f49f964f976f3842bda000aef31486","messageID":"msg_g001a087d61247001eDqV40rEk","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":28944,"input":28772,"output":172,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}} +{"type":"step_start","timestamp":1788985379644,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d68b3a0016Fc45pZx7","messageID":"msg_g001a087d64c75001IONestjtU","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"4c50677592c0f46e55158ad6d71787144d649bb2","type":"step-start"}} +{"type":"text","timestamp":1788985380277,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d68c26001BJW6Lkjew","messageID":"msg_g001a087d64c75001IONestjtU","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"text","text":"The prior OUTWARD attempt for this stage failed its gate; I'll run the discovery fresh with real searches. Next: find all call sites of the `mark_*` telemetry markers and locate the StageAttempt usage in the production path.","time":{"start":1788985379878,"end":1788985380275}}} +{"type":"tool_use","timestamp":1788985380293,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"grep","callID":"call_98aea15451f14ef79e790439","state":{"status":"completed","input":{"pattern":"StageAttempt|stage_telemetry"},"output":"Found 47 matches\n/mnt/workdrive/issue161-harness/research/issue-310/stage_OUTWARD.events.jsonl:\n Line 7: {\"type\":\"tool_use\",\"timestamp\":1788985362928,\"sessionID\":\"ses_-ffe5f782a30b9ffeXheAHMyyt\",\"part\":{\"type\":\"tool\",\"tool\":\"grep\",\"callID\":\"call_8092e5f1bc0e42a2a9898330\",\"state\":{\"status\":\"completed\",\"input\":{\"pattern\":\"model_time|tool_time\"},\"output\":\"Found 51 matches\\n/mnt/workdrive/issue161-harness/research/issue-310/task_tree.json:\\n Line 13: \\\"title\\\": \\\"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\",\\n\\n Line 16: \\\"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\"\\n\\n Line 53: \\\"model_time\\\",\\n\\n Line 54: \\\"tool_time\\\",\\n\\n Line 133: \\\"title\\\": \\\"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\",\\n\\n Line 136: \\\"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\"\\n\\n\\n/mnt/workdrive/issue161-harness/research/issue-310/telemetry/attempts.jsonl:\\n Line 1: {\\\"attempt_id\\\": \\\"issue-310-ISSUE-1-a1\\\", \\\"event_id\\\": \\\"issue-310-ISSUE-1\\\", \\\"stage\\\": \\\"ISSUE\\\", \\\"issue\\\": 310, \\\"process_id\\\": \\\"research-310-r1\\\", \\\"started_at\\\": \\\"2026-09-09T20:15:09.949030+00:00\\\", \\\"finished_at\\\": \\\"2026-09-09T20:15:10.108621+00:00\\\", \\\"terminal_status\\\": \\\"COMPLETED\\\", \\\"wall_time_s\\\": 0.1597, \\\"model_time_s\\\": 0, \\\"tool_time_s\\\": 0, \\\"gate_time_s\\\": 0, \\\"persistence_time_s\\\": 0, \\\"overlap_note\\\": \\\"categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive\\\", \\\"model_calls\\\": 0, \\\"tool_calls\\\": 0, \\\"repo_read_calls\\\": 0, \\\"external_queries\\\": 0, \\\"sources_discovered\\\": 0, \\\"sources_read\\\": 0, \\\"retries\\\": 0, \\\"reconciliation_actions\\\": 0, \\\"implementation_corrections\\\": 0, \\\"ci_failures\\\": 0, \\\"input_tokens\\\": \\\"UNKNOWN_NOT_EXPOSED\\\", \\\"output_tokens\\\": \\\"UNKNOWN_NOT_EXPOSED\\\", \\\"queue_wait_s...\n Line 11: {\"type\":\"text\",\"timestamp\":1788985380277,\"sessionID\":\"ses_-ffe5f782a30b9ffeXheAHMyyt\",\"part\":{\"id\":\"prt_g001a087d68c26001BJW6Lkjew\",\"messageID\":\"msg_g001a087d64c75001IONestjtU\",\"sessionID\":\"ses_-ffe5f782a30b9ffeXheAHMyyt\",\"type\":\"text\",\"text\":\"The prior OUTWARD attempt for this stage failed its gate; I'll run the discovery fresh with real searches. Next: find all call sites of the `mark_*` telemetry markers and locate the StageAttempt usage in the production path.\",\"time\":{\"start\":1788985379878,\"end\":1788985380275}}}\n\n\n/mnt/workdrive/issue161-harness/research/issue-310/outward.json:\n Line 13: \"receipt\": \"src/rune/rune_operator.py:1574; src/rune/inference_trace.py:69; src/rune/canary_p0_07.py:57,162,195,224 (7 files total incl. 4 tests); scripts/stage_telemetry.py:13,140,141,155,156\"\n\n Line 17: \"query\": \"rg 'StageAttempt|TelemetryLedger' (whole repo, *.py)\",\n\n Line 19: \"receipt\": \"27 matches: scripts/stage_telemetry.py (self), scripts/research_stage_executor.py:87-91 (sole production importer), tests/test_stage_telemetry.py (test only)\"\n\n Line 25: \"receipt\": \"10 matches, ALL inside scripts/stage_telemetry.py (definitions+docstring); zero call sites in scripts/; src/ has zero matches (separate grep Q1-adjacent)\"\n\n Line 31: \"receipt\": \"src/ telemetry: only src/rune/orchestration/capability.py:24 (string literal 'telemetry' in capability list); StageAttempt-category names: 0 matches in src/\"\n\n Line 35: \"query\": \"grep stage_telemetry importers across scripts/ src/ tests/ (bash grep)\",\n\n Line 55: \"path\": \"scripts/stage_telemetry.py\",\n\n Line 58: \"classification\": \"PRIMARY — the StageAttempt telemetry module (OBS-1, epic #284/#287); defines mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end, summary() deriving model_time_s/tool_time_s from summed windows\"\n\n Line 65: \"classification\": \"PRIMARY — sole production importer of stage_telemetry (lines 87-91); constructs StageAttempt + TelemetryLedger per stage event, calls only finish() + persist()\"\n\n Line 69: \"path\": \"tests/test_stage_telemetry.py\",\n\n Line 79: \"classification\": \"ADJACENT — runs the actual mimo worker subprocess for OUTWARD; carries its own wall-clock 'elapsed' but has zero telemetry/StageAttempt hooks; the layer where model/tool time actually accrues yet is unmarked\"\n\n Line 86: \"classification\": \"DISTINCT-SCHEMA — InferenceTrace.model_time_ms (line 69) is a different telemetry line (Ollama total_duration via transport accumulators), NOT the StageAttempt model_time_s; named similarly, easily conflated\"\n\n Line 93: \"classification\": \"DISTINCT-SCHEMA — writes InferenceTrace.model_time_ms = run_usage duration_ns / 1e6 (line 1574); this is the model-side timing that DOES work in Rune's operator path, unlike StageAttempt's model_time_s\"\n\n Line 119: \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish().\",\n\n Line 120: \"locus\": \"scripts/stage_telemetry.py:83-128, 140-143\"\n\n Line 125: \"claim\": \"The executor is the ONLY production code importing stage_telemetry; it wraps every stage with StageAttempt(...)/finish(...)/ledger.persist(...) but NEVER calls any mark_* window marker between construction and finish.\",\n\n Line 131: \"claim\": \"mark_model_start/mark_model_end/mark_tool_start/mark_tool_end are invoked only in tests/test_stage_telemetry.py; no call sites exist anywhere in scripts/ outside stage_telemetry.py itself and none in src/.\",\n\n Line 132: \"locus\": \"tests/test_stage_telemetry.py:62-63, 77\"\n\n Line 144: \"locus\": \"scripts/stage_telemetry.py:132-141, 194-206; scripts/research_stage_executor.py:87-101\"\n\n Line 149: \"claim\": \"The OUTWARD stage's actual model invocation happens inside a mimo subprocess driven by ResearchEventRelay, which records only a coarse wall-clock 'elapsed' and has no StageAttempt hooks — the instrumentation gap is precisely at the executor/relay boundary.\",\n\n Line 155: \"claim\": \"A similarly-named but separate timing surface exists and DOES function: InferenceTrace.model_time_ms is populated from Ollama transport usage_total_duration_ns in the operator path (src/rune/rune_operator.py:1574). This is NOT StageAttempt.model_time_s; conflation risk when reading ADAPT-1 claims.\",\n\n Line 161: \"claim\": \"stage_telemetry.py self-documents the design constraint: observational only, every attempt persists its own record (incl. failures), categories observed and non-additive vs wall_time — the executor's finish() status mapping (COMPLETED/GATE_FAILED/FAILED/INTERRUPTED) honors the first and last of these; TIMEOUT mapping is not visible in the executor's mapping expression.\",\n\n Line 162: \"locus\": \"scripts/stage_telemetry.py:2-28, 37; scripts/research_stage_executor.py:96-100\"\n\n Line 174: \"window_markers in relay-side subprocess events were not parsed from stage_OUTWARD.events.jsonl; the relay has no StageAttempt references (grep-verified), so no additional marker call sites could hide there.\",\n\n Line 175: \"Grep receipts cover *.py files; a marker invocation from a non-Python file (e.g. shell workflow) was not searched — no such workflow scripts reference stage_telemetry based on the repo-wide importer grep.\",\n\n Line 176: \"TIMEOUT terminal-status handling: stage_telemetry.py defines TIMEOUT in TERMINAL_STATUSES and force-closes windows on interrupt paths, but the executor's finish() expression does not map a TIMEOUT status; whether any caller can currently produce TIMEOUT was not traced further (out of B1 scope).\"\n\n Line 180: \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (works, populated from transport accumulators) vs StageAttempt.model_time_s (always 0) — same 'model_time' family, different pipelines; a source citing 'model_time is populated' would be true of the former and false of the latter.\"\n\n Line 185: {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"StageAttempt\", \"scope\": \"src/\", \"hits\": 0},\n\n Line 187: {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"stage_telemetry|StageAttempt|mark_model_start|mark_tool_start|TelemetryLedger\", \"scope\": \"repo *.py\", \"hits\": 27},\n\n Line 189: {\"kind\": \"QUERY\", \"tool\": \"bash grep\", \"pattern\": \"stage_telemetry importers\", \"scope\": \"scripts/ src/ tests/\", \"result\": \"1 importer\"},\n\n Line 190: {\"kind\": \"SOURCE_READ\", \"tool\": \"Read\", \"target\": \"scripts/stage_telemetry.py\", \"lines\": \"1-221 (full)\"},\n\n\n/mnt/workdrive/issue161-harness/scripts/research_stage_executor.py:\n Line 87: from stage_telemetry import StageAttempt, TelemetryLedger\n\n Line 89: _attempt = StageAttempt(attempt_id, event_id, stage,\n\n\n/mnt/workdrive/issue161-harness/scripts/stage_telemetry.py:\n Line 2: \"\"\"stage_telemetry.py — StageAttempt telemetry (OBS-1, epic #284 / #287).\n\n Line 45: class StageAttempt:\n\n Line 194: def persist(self, attempt: StageAttempt) -> bool:\n\n\n/mnt/workdrive/issue161-harness/tests/test_stage_telemetry.py:\n Line 1: \"\"\"Tests: OBS-1 StageAttempt telemetry (#287).\n\n Line 25: (\"stage_telemetry\", \"scripts/stage_telemetry.py\"),\n\n Line 35: st = sys.modules[\"stage_telemetry\"]\n\n Line 61: a = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", 1, \"research-1-r1\")\n\n Line 76: a = st.StageAttempt(\"A2\", \"E2\", \"OUTWARD\", 1, \"p\")\n\n Line 85: a = st.StageAttempt(\"A3\", \"E3\", \"X\", 1, \"p\")\n\n Line 93: a1 = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", tree.issue, \"p\")\n\n Line 95: a2 = st.StageAttempt(\"A2\", \"E2\", \"OUTWARD\", tree.issue, \"p\")\n\n Line 109: a = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", tree.issue, \"p\")\n","metadata":{"matches":47,"truncated":false},"title":"StageAttempt|stage_telemetry","time":{"start":1788985380278,"end":1788985380292}},"id":"prt_g001a087d68c81001Zw2o2uywz","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d64c75001IONestjtU"}} +{"type":"tool_use","timestamp":1788985380297,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"grep","callID":"call_9bd939f8b8fe431ca886675b","state":{"status":"completed","input":{"pattern":"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start"},"output":"Found 16 matches\n/mnt/workdrive/issue161-harness/research/issue-310/stage_OUTWARD.events.jsonl:\n Line 7: {\"type\":\"tool_use\",\"timestamp\":1788985362928,\"sessionID\":\"ses_-ffe5f782a30b9ffeXheAHMyyt\",\"part\":{\"type\":\"tool\",\"tool\":\"grep\",\"callID\":\"call_8092e5f1bc0e42a2a9898330\",\"state\":{\"status\":\"completed\",\"input\":{\"pattern\":\"model_time|tool_time\"},\"output\":\"Found 51 matches\\n/mnt/workdrive/issue161-harness/research/issue-310/task_tree.json:\\n Line 13: \\\"title\\\": \\\"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\",\\n\\n Line 16: \\\"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\"\\n\\n Line 53: \\\"model_time\\\",\\n\\n Line 54: \\\"tool_time\\\",\\n\\n Line 133: \\\"title\\\": \\\"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\",\\n\\n Line 136: \\\"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\\\u2014 model_time/tool_time always 0\\\"\\n\\n\\n/mnt/workdrive/issue161-harness/research/issue-310/telemetry/attempts.jsonl:\\n Line 1: {\\\"attempt_id\\\": \\\"issue-310-ISSUE-1-a1\\\", \\\"event_id\\\": \\\"issue-310-ISSUE-1\\\", \\\"stage\\\": \\\"ISSUE\\\", \\\"issue\\\": 310, \\\"process_id\\\": \\\"research-310-r1\\\", \\\"started_at\\\": \\\"2026-09-09T20:15:09.949030+00:00\\\", \\\"finished_at\\\": \\\"2026-09-09T20:15:10.108621+00:00\\\", \\\"terminal_status\\\": \\\"COMPLETED\\\", \\\"wall_time_s\\\": 0.1597, \\\"model_time_s\\\": 0, \\\"tool_time_s\\\": 0, \\\"gate_time_s\\\": 0, \\\"persistence_time_s\\\": 0, \\\"overlap_note\\\": \\\"categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive\\\", \\\"model_calls\\\": 0, \\\"tool_calls\\\": 0, \\\"repo_read_calls\\\": 0, \\\"external_queries\\\": 0, \\\"sources_discovered\\\": 0, \\\"sources_read\\\": 0, \\\"retries\\\": 0, \\\"reconciliation_actions\\\": 0, \\\"implementation_corrections\\\": 0, \\\"ci_failures\\\": 0, \\\"input_tokens\\\": \\\"UNKNOWN_NOT_EXPOSED\\\", \\\"output_tokens\\\": \\\"UNKNOWN_NOT_EXPOSED\\\", \\\"queue_wait_s...\n\n/mnt/workdrive/issue161-harness/research/issue-310/outward.json:\n Line 58: \"classification\": \"PRIMARY — the StageAttempt telemetry module (OBS-1, epic #284/#287); defines mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end, summary() deriving model_time_s/tool_time_s from summed windows\"\n\n Line 72: \"classification\": \"TEST_ONLY — the ONLY place mark_model_start/mark_tool_start are ever invoked (e.g. lines 62-63, 77); confirms markers are exercised exclusively in tests\"\n\n Line 131: \"claim\": \"mark_model_start/mark_model_end/mark_tool_start/mark_tool_end are invoked only in tests/test_stage_telemetry.py; no call sites exist anywhere in scripts/ outside stage_telemetry.py itself and none in src/.\",\n\n Line 187: {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"stage_telemetry|StageAttempt|mark_model_start|mark_tool_start|TelemetryLedger\", \"scope\": \"repo *.py\", \"hits\": 27},\n\n\n/mnt/workdrive/issue161-harness/tests/test_stage_telemetry.py:\n Line 62: a.mark_model_start(); a.mark_model_end()\n\n Line 63: a.mark_tool_start(); a.mark_tool_end()\n\n Line 77: a.mark_model_start()\n\n\n/mnt/workdrive/issue161-harness/scripts/stage_telemetry.py:\n Line 24: handle.mark_model_start/end() → model invocation windows\n\n Line 25: handle.mark_tool_start/end() → tool execution windows\n\n Line 83: def mark_model_start(self) -> None:\n\n Line 86: def mark_model_end(self) -> None:\n\n Line 92: def mark_tool_start(self) -> None:\n\n Line 95: def mark_tool_end(self) -> None:\n\n Line 101: def mark_gate_start(self) -> None:\n\n Line 109: def mark_persistence_start(self) -> None:\n","metadata":{"matches":16,"truncated":false},"title":"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start","time":{"start":1788985380276,"end":1788985380295}},"id":"prt_g001a087d68c2d001Ff5cnQj8i","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d64c75001IONestjtU"}} +{"type":"step_finish","timestamp":1788985380320,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d68dc8001biGt36Z5R","reason":"tool-calls","snapshot":"2fccfea23d5910b09c340704630276479a3b4090","messageID":"msg_g001a087d64c75001IONestjtU","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":32057,"input":31846,"output":211,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}} +{"type":"step_start","timestamp":1788985395039,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d6c75b001w3KzgVEy3","messageID":"msg_g001a087d68e0800100ieT3dki","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"e147cbbc339d3c2941e3d7acbc4c2c60eaaa39d3","type":"step-start"}} +{"type":"tool_use","timestamp":1788985395891,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"read","callID":"call_d210b4d227f64e839a621fa6","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/tests/test_stage_telemetry.py","limit":60,"offset":50},"output":"/mnt/workdrive/issue161-harness/tests/test_stage_telemetry.py\nfile\n\n50: \n51: def _executor(tree):\n52: return rse.StageExecutor(issue=tree.issue, artifact_dir=tree.artifact_dir, round_no=tree.round)\n53: \n54: \n55: def _ledger(tree):\n56: return st.TelemetryLedger(tree.artifact_dir)\n57: \n58: \n59: def test_unit_attempt_lifecycle(tmp_path):\n60: \"\"\"Unit: begin → windows → finish produces a complete record.\"\"\"\n61: a = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", 1, \"research-1-r1\")\n62: a.mark_model_start(); a.mark_model_end()\n63: a.mark_tool_start(); a.mark_tool_end()\n64: a.finish(\"COMPLETED\")\n65: s = a.summary()\n66: assert s[\"terminal_status\"] == \"COMPLETED\"\n67: assert s[\"model_calls\"] == 1 and s[\"tool_calls\"] == 1\n68: assert s[\"input_tokens\"] == \"UNKNOWN_NOT_EXPOSED\"\n69: assert s[\"wall_time_s\"] is not None\n70: # Non-additive note present\n71: assert \"not additive\" in s[\"overlap_note\"]\n72: \n73: \n74: def test_unclosed_windows_closed_on_interrupt(tmp_path):\n75: \"\"\"INTERRUPTED path: unclosed windows are closed at finish(), not lost.\"\"\"\n76: a = st.StageAttempt(\"A2\", \"E2\", \"OUTWARD\", 1, \"p\")\n77: a.mark_model_start()\n78: a.finish(\"INTERRUPTED\")\n79: assert a.summary()[\"window_counts\"][\"model\"] == 1\n80: s, e = a.model_windows[0]\n81: assert e > s\n82: \n83: \n84: def test_invalid_terminal_status_rejected():\n85: a = st.StageAttempt(\"A3\", \"E3\", \"X\", 1, \"p\")\n86: with pytest.raises(AssertionError):\n87: a.finish(\"MAYBE\")\n88: \n89: \n90: def test_ledger_persists_and_reloads(tmp_path):\n91: tree = _make_tree(tmp_path)\n92: led = _ledger(tree)\n93: a1 = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", tree.issue, \"p\")\n94: a1.finish(\"COMPLETED\")\n95: a2 = st.StageAttempt(\"A2\", \"E2\", \"OUTWARD\", tree.issue, \"p\")\n96: a2.finish(\"GATE_FAILED\")\n97: assert led.persist(a1) and led.persist(a2)\n98: # Reload survives \"restart\" (new ledger instance)\n99: led2 = st.TelemetryLedger(tree.artifact_dir)\n100: recs = led2.load_all()\n101: assert len(recs) == 2\n102: assert {r[\"terminal_status\"] for r in recs} == {\"COMPLETED\", \"GATE_FAILED\"}\n103: \n104: \n105: def test_ledger_persist_never_raises(tmp_path, monkeypatch):\n106: \"\"\"Observational invariant: a broken telemetry sink must never break the stage.\"\"\"\n107: tree = _make_tree(tmp_path)\n108: led = _ledger(tree)\n109: a = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", tree.issue, \"p\")\n\n(Showing lines 50-109 of 194. Use offset=110 to continue.)\n","metadata":{"preview":"\ndef _executor(tree):\n return rse.StageExecutor(issue=tree.issue, artifact_dir=tree.artifact_dir, round_no=tree.round)\n\n\ndef _ledger(tree):\n return st.TelemetryLedger(tree.artifact_dir)\n\n\ndef test_unit_attempt_lifecycle(tmp_path):\n \"\"\"Unit: begin → windows → finish produces a complete record.\"\"\"\n a = st.StageAttempt(\"A1\", \"E1\", \"OUTWARD\", 1, \"research-1-r1\")\n a.mark_model_start(); a.mark_model_end()\n a.mark_tool_start(); a.mark_tool_end()\n a.finish(\"COMPLETED\")\n s = a.summary()\n assert s[\"terminal_status\"] == \"COMPLETED\"\n assert s[\"model_calls\"] == 1 and s[\"tool_calls\"] == 1\n assert s[\"input_tokens\"] == \"UNKNOWN_NOT_EXPOSED\"\n assert s[\"wall_time_s\"] is not None","truncated":true,"loaded":[]},"title":"tests/test_stage_telemetry.py","time":{"start":1788985395882,"end":1788985395890}},"id":"prt_g001a087d6c896001X7cfbqjWO","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d68e0800100ieT3dki"}} +{"type":"tool_use","timestamp":1788985395900,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"read","callID":"call_79f6a0ce6b0c43cb8fb28438","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/scripts/research_stage_executor.py","limit":70,"offset":60},"output":"/mnt/workdrive/issue161-harness/scripts/research_stage_executor.py\nfile\n\n60: def execute(self, event: dict) -> dict:\n61: \"\"\"Execute exactly one stage based on the ResearchStageReady event.\"\"\"\n62: stage = event[\"stage\"]\n63: attempt = event.get(\"attempt\", 1)\n64: event_id = event.get(\"event_id\", make_event_id(\n65: self.issue, f\"research-{self.issue}\", self.round_no, stage, attempt))\n66: \n67: # Idempotency check: has this event already been consumed?\n68: consumed_path = Path(self.artifact_dir) / f\".consumed_{event_id}\"\n69: if consumed_path.exists():\n70: return {\n71: \"status\": \"DUPLICATE\",\n72: \"event_id\": event_id,\n73: \"message\": f\"Event {event_id} already consumed — no-op\",\n74: }\n75: \n76: # Mark as consumed (idempotency)\n77: consumed_path.parent.mkdir(parents=True, exist_ok=True)\n78: consumed_path.write_text(f\"consumed at {time.time()}\")\n79: \n80: self.event_log.append({\n81: \"event_id\": event_id, \"stage\": stage, \"attempt\": attempt,\n82: \"timestamp\": time.time(), \"action\": \"ACK\",\n83: })\n84: \n85: # OBS-1 telemetry: observational — every attempt records its own\n86: # lifecycle (successes AND failures); never changes control flow.\n87: from stage_telemetry import StageAttempt, TelemetryLedger\n88: attempt_id = f\"{event_id}-a{attempt}\"\n89: _attempt = StageAttempt(attempt_id, event_id, stage,\n90: self.issue, f\"research-{self.issue}-r{self.round_no}\")\n91: _ledger = TelemetryLedger(self.artifact_dir)\n92: \n93: # Execute ONLY this stage\n94: result = self._execute_stage(stage, attempt, event)\n95: \n96: _attempt.finish(\n97: \"COMPLETED\" if result.get(\"status\") == \"STAGE_COMPLETED\"\n98: else (\"GATE_FAILED\" if result.get(\"status\") in (\"GATE_FAILED\", \"GATE_RETRY\")\n99: else (\"FAILED\" if result.get(\"status\", \"\").endswith(\"FAILED\")\n100: else result.get(\"status\", \"INTERRUPTED\"))))\n101: _ledger.persist(_attempt)\n102: \n103: # Runner-placement gate (RP2/#265): capture WHERE this actually ran\n104: # and reject Pi-executed desktop-heavy stages as valid acceptance.\n105: from runner_evidence import capture_execution_evidence, validate_placement, summarize_for_artifact\n106: evidence = capture_execution_evidence(\n107: stage=stage, issue=self.issue,\n108: process_id=f\"research-{self.issue}-r{self.round_no}\",\n109: )\n110: placement_ok, placement_violations = validate_placement(evidence)\n111: result[\"runner_evidence\"] = summarize_for_artifact(evidence)\n112: if not placement_ok:\n113: result[\"status\"] = \"PLACEMENT_REJECTED\"\n114: result[\"placement_violations\"] = placement_violations\n115: self.tree.fail(\"; \".join(placement_violations))\n116: \n117: # Record the result\n118: self.event_log.append({\n119: \"event_id\": event_id, \"stage\": stage, \"attempt\": attempt,\n120: \"timestamp\": time.time(),\n121: \"action\": result[\"status\"],\n122: \"artifact_hash\": result.get(\"artifact_hash\", \"\"),\n123: })\n124: \n125: # Persist the tree\n126: self.tree.save()\n127: \n128: return result\n129: \n\n(Showing lines 60-129 of 1532. Use offset=130 to continue.)\n","metadata":{"preview":" def execute(self, event: dict) -> dict:\n \"\"\"Execute exactly one stage based on the ResearchStageReady event.\"\"\"\n stage = event[\"stage\"]\n attempt = event.get(\"attempt\", 1)\n event_id = event.get(\"event_id\", make_event_id(\n self.issue, f\"research-{self.issue}\", self.round_no, stage, attempt))\n\n # Idempotency check: has this event already been consumed?\n consumed_path = Path(self.artifact_dir) / f\".consumed_{event_id}\"\n if consumed_path.exists():\n return {\n \"status\": \"DUPLICATE\",\n \"event_id\": event_id,\n \"message\": f\"Event {event_id} already consumed — no-op\",\n }\n\n # Mark as consumed (idempotency)\n consumed_path.parent.mkdir(parents=True, exist_ok=True)\n consumed_path.write_text(f\"consumed at {time.time()}\")\n","truncated":true,"loaded":[]},"title":"scripts/research_stage_executor.py","time":{"start":1788985395879,"end":1788985395898}},"id":"prt_g001a087d6c75e0017Q0gGxP5P","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d68e0800100ieT3dki"}} +{"type":"step_finish","timestamp":1788985395920,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d6cabc001wYsUjyeYZ","reason":"tool-calls","snapshot":"e2de1549558c0813784dca0a88488aa5ab796be4","messageID":"msg_g001a087d68e0800100ieT3dki","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":35931,"input":35856,"output":75,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}} +{"type":"step_start","timestamp":1788985408614,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d6fc64001p0bKKeOQ2","messageID":"msg_g001a087d6caef001cSg8q8GcD","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"1d475c53d62e034c29684f75a7554b3f665de1f5","type":"step-start"}} +{"type":"tool_use","timestamp":1788985409730,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"grep","callID":"call_b0ad87c734ae4570bc3042aa","state":{"status":"completed","input":{"include":"scripts/research_stage_executor.py","pattern":"ResearchEventRelay|elapsed"},"output":"Found 20 matches\n/mnt/workdrive/issue161-harness/scripts/research_stage_executor.py:\n Line 363: from research_event_relay import ResearchEventRelay\n\n Line 364: relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)\n\n Line 501: \"elapsed\": relay_result[\"elapsed\"],\n\n Line 585: from research_event_relay import ResearchEventRelay\n\n Line 586: relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)\n\n Line 658: \"elapsed\": relay_result.get(\"elapsed\", 0.0),\n\n Line 755: from research_event_relay import ResearchEventRelay\n\n Line 756: relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)\n\n Line 811: \"elapsed\": relay_result.get(\"elapsed\", 0.0),\n\n Line 917: from research_event_relay import ResearchEventRelay\n\n Line 918: relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)\n\n Line 964: \"elapsed\": relay_result.get(\"elapsed\", 0.0),\n\n Line 1072: from research_event_relay import ResearchEventRelay\n\n Line 1073: relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)\n\n Line 1182: from research_event_relay import ResearchEventRelay\n\n Line 1183: relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)\n\n Line 1215: relay_result = {\"returncode\": 0, \"texts\": [], \"event_counts\": {}, \"receipts\": [], \"elapsed\": 0.0}\n\n Line 1243: \"elapsed\": relay_result.get(\"elapsed\", 0.0),\n\n Line 1306: \"elapsed\": relay_result.get(\"elapsed\", 0.0),\n\n Line 1407: \"elapsed\": relay_result[\"elapsed\"],\n","metadata":{"matches":20,"truncated":false},"title":"ResearchEventRelay|elapsed","time":{"start":1788985409720,"end":1788985409729}},"id":"prt_g001a087d6fe450011j6936Fva","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d6caef001cSg8q8GcD"}} +{"type":"tool_use","timestamp":1788985409735,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"read","callID":"call_10c53cfd725c42ff83c83db3","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/scripts/stage_telemetry.py"},"output":"/mnt/workdrive/issue161-harness/scripts/stage_telemetry.py\nfile\n\n1: #!/usr/bin/env python3\n2: \"\"\"stage_telemetry.py — StageAttempt telemetry (OBS-1, epic #284 / #287).\n3: \n4: OBSERVATIONAL ONLY. This module records what happened; it must never change\n5: stage authorization, retry behavior, model prompts, runner selection, or gate\n6: outcomes. Timing code is not a process authority.\n7: \n8: Design decisions (ratified in #287 tightening):\n9: - EVERY attempt persists its own lifecycle record — successful, gate-failed,\n10: failed, timed-out, interrupted. Dropping expensive failures would bias\n11: ADAPT-1's baseline toward cheap success.\n12: - Timing categories are OBSERVED, not forced additive: wall_time is not\n13: model_time + tool_time when MiMo waits on a tool or relay overhead overlaps.\n14: Records carry the raw timestamps so reports can distinguish exclusive /\n15: overlapping / unmeasured-residual.\n16: - Records survive restart: each attempt is appended to\n17: /telemetry/attempts.jsonl (append-only, one JSON object per\n18: line) AND mirrored into the task-tree state at persist time.\n19: - UNKNOWN_NOT_EXPOSED is recorded verbatim when a counter (e.g. provider\n20: tokens) is unavailable. Estimates are never presented as measurements.\n21: \n22: Attempt lifecycle (from the executor's perspective):\n23: begin(attempt_id, event_id, stage) → returns a handle\n24: handle.mark_model_start/end() → model invocation windows\n25: handle.mark_tool_start/end() → tool execution windows\n26: handle.finish(terminal_status) → COMPLETED | GATE_FAILED | FAILED |\n27: TIMEOUT | INTERRUPTED\n28: persist(handle) → append + mirror\n29: \"\"\"\n30: from __future__ import annotations\n31: \n32: import json\n33: import os\n34: import time\n35: from datetime import datetime, timezone\n36: \n37: TERMINAL_STATUSES = {\"COMPLETED\", \"GATE_FAILED\", \"FAILED\", \"TIMEOUT\", \"INTERRUPTED\"}\n38: UNKNOWN = \"UNKNOWN_NOT_EXPOSED\"\n39: \n40: \n41: def _now_iso() -> str:\n42: return datetime.now(timezone.utc).isoformat()\n43: \n44: \n45: class StageAttempt:\n46: \"\"\"One attempt's observed lifecycle. Pure data — no control flow.\"\"\"\n47: \n48: def __init__(self, attempt_id: str, event_id: str, stage: str,\n49: issue: int, process_id: str):\n50: self.attempt_id = attempt_id\n51: self.event_id = event_id\n52: self.stage = stage\n53: self.issue = issue\n54: self.process_id = process_id\n55: self.started_at = _now_iso()\n56: self._t0 = time.monotonic()\n57: self.finished_at: str | None = None\n58: self.terminal_status: str | None = None\n59: # Observed windows (monotonic pairs, converted at finish)\n60: self.model_windows: list[tuple[float, float]] = []\n61: self.tool_windows: list[tuple[float, float]] = []\n62: self.gate_windows: list[tuple[float, float]] = []\n63: self.persistence_windows: list[tuple[float, float]] = []\n64: # Counters\n65: self.model_calls = 0\n66: self.tool_calls = 0\n67: self.repo_read_calls = 0\n68: self.external_queries = 0\n69: self.sources_discovered = 0\n70: self.sources_read = 0\n71: self.retries = 0\n72: self.reconciliation_actions = 0\n73: self.implementation_corrections = 0\n74: self.ci_failures = 0\n75: # Provider counters — recorded as UNKNOWN unless actually exposed\n76: self.input_tokens: int | str = UNKNOWN\n77: self.output_tokens: int | str = UNKNOWN\n78: # Queue wait: measured by the caller if it knows the dispatch time\n79: self.queue_wait_s: float | None = None\n80: \n81: # -- window markers (observational; no control flow) ---------------------\n82: \n83: def mark_model_start(self) -> None:\n84: self.model_windows.append((time.monotonic(), 0.0))\n85: \n86: def mark_model_end(self) -> None:\n87: if self.model_windows and self.model_windows[-1][1] == 0.0:\n88: s, _ = self.model_windows[-1]\n89: self.model_windows[-1] = (s, time.monotonic())\n90: self.model_calls += 1\n91: \n92: def mark_tool_start(self) -> None:\n93: self.tool_windows.append((time.monotonic(), 0.0))\n94: \n95: def mark_tool_end(self) -> None:\n96: if self.tool_windows and self.tool_windows[-1][1] == 0.0:\n97: s, _ = self.tool_windows[-1]\n98: self.tool_windows[-1] = (s, time.monotonic())\n99: self.tool_calls += 1\n100: \n101: def mark_gate_start(self) -> None:\n102: self.gate_windows.append((time.monotonic(), 0.0))\n103: \n104: def mark_gate_end(self) -> None:\n105: if self.gate_windows and self.gate_windows[-1][1] == 0.0:\n106: s, _ = self.gate_windows[-1]\n107: self.gate_windows[-1] = (s, time.monotonic())\n108: \n109: def mark_persistence_start(self) -> None:\n110: self.persistence_windows.append((time.monotonic(), 0.0))\n111: \n112: def mark_persistence_end(self) -> None:\n113: if self.persistence_windows and self.persistence_windows[-1][1] == 0.0:\n114: s, _ = self.persistence_windows[-1]\n115: self.persistence_windows[-1] = (s, time.monotonic())\n116: \n117: # -- terminal ------------------------------------------------------------\n118: \n119: def finish(self, terminal_status: str) -> None:\n120: assert terminal_status in TERMINAL_STATUSES, terminal_status\n121: self.terminal_status = terminal_status\n122: self.finished_at = _now_iso()\n123: # Close any unclosed windows (INTERRUPTED/TIMEOUT paths)\n124: for lst in (self.model_windows, self.tool_windows,\n125: self.gate_windows, self.persistence_windows):\n126: if lst and lst[-1][1] == 0.0:\n127: s, _ = lst[-1]\n128: lst[-1] = (s, time.monotonic())\n129: \n130: # -- derived (observed, explicitly non-additive) --------------------------\n131: \n132: def _sum_windows(self, windows: list[tuple[float, float]]) -> float:\n133: return round(sum(e - s for s, e in windows), 4)\n134: \n135: def summary(self) -> dict:\n136: # wall_time: monotonic delta while running; after finish, derived from\n137: # the started/finished ISO pair is NOT monotonic-safe — keep the\n138: # monotonic delta captured at finish time instead.\n139: wall = round(time.monotonic() - self._t0, 4)\n140: model_time = self._sum_windows(self.model_windows)\n141: tool_time = self._sum_windows(self.tool_windows)\n142: gate_time = self._sum_windows(self.gate_windows)\n143: persistence_time = self._sum_windows(self.persistence_windows)\n144: return {\n145: \"attempt_id\": self.attempt_id,\n146: \"event_id\": self.event_id,\n147: \"stage\": self.stage,\n148: \"issue\": self.issue,\n149: \"process_id\": self.process_id,\n150: \"started_at\": self.started_at,\n151: \"finished_at\": self.finished_at,\n152: \"terminal_status\": self.terminal_status,\n153: \"wall_time_s\": wall,\n154: # Observed categories — NOT asserted to sum to wall_time.\n155: \"model_time_s\": model_time,\n156: \"tool_time_s\": tool_time,\n157: \"gate_time_s\": gate_time,\n158: \"persistence_time_s\": persistence_time,\n159: # Overlap analysis: tool time inside model windows is overlap,\n160: # not double-counted work.\n161: \"overlap_note\": (\n162: \"categories are observed windows and may overlap; \"\n163: \"wall_time is authoritative, category sums are not additive\"),\n164: \"model_calls\": self.model_calls,\n165: \"tool_calls\": self.tool_calls,\n166: \"repo_read_calls\": self.repo_read_calls,\n167: \"external_queries\": self.external_queries,\n168: \"sources_discovered\": self.sources_discovered,\n169: \"sources_read\": self.sources_read,\n170: \"retries\": self.retries,\n171: \"reconciliation_actions\": self.reconciliation_actions,\n172: \"implementation_corrections\": self.implementation_corrections,\n173: \"ci_failures\": self.ci_failures,\n174: \"input_tokens\": self.input_tokens,\n175: \"output_tokens\": self.output_tokens,\n176: \"queue_wait_s\": self.queue_wait_s,\n177: \"window_counts\": {\n178: \"model\": len(self.model_windows),\n179: \"tool\": len(self.tool_windows),\n180: \"gate\": len(self.gate_windows),\n181: \"persistence\": len(self.persistence_windows),\n182: },\n183: }\n184: \n185: \n186: class TelemetryLedger:\n187: \"\"\"Append-only attempt ledger. Survives restart (JSONL on disk).\n188: Appending is observational: a failed append must never break the stage.\"\"\"\n189: \n190: def __init__(self, artifact_dir: str):\n191: self.dir = os.path.join(artifact_dir, \"telemetry\")\n192: self.path = os.path.join(self.dir, \"attempts.jsonl\")\n193: \n194: def persist(self, attempt: StageAttempt) -> bool:\n195: \"\"\"Append one attempt record. Returns True on success; NEVER raises\n196: into the stage path (observational invariant).\"\"\"\n197: try:\n198: os.makedirs(self.dir, exist_ok=True)\n199: with open(self.path, \"a\") as f:\n200: f.write(json.dumps(attempt.summary(), default=str) + \"\\n\")\n201: return True\n202: except (OSError, TypeError, ValueError):\n203: # Observational invariant: telemetry failures (unwritable path,\n204: # serialization problems) are swallowed, never raised into the\n205: # stage path.\n206: return False\n207: \n208: def load_all(self) -> list[dict]:\n209: if not os.path.exists(self.path):\n210: return []\n211: out = []\n212: with open(self.path) as f:\n213: for line in f:\n214: try:\n215: out.append(json.loads(line))\n216: except json.JSONDecodeError:\n217: continue\n218: return out\n219: \n220: def attempts_for(self, stage: str) -> list[dict]:\n221: return [a for a in self.load_all() if a.get(\"stage\") == stage]\n\n(End of file - total 221 lines)\n","metadata":{"preview":"#!/usr/bin/env python3\n\"\"\"stage_telemetry.py — StageAttempt telemetry (OBS-1, epic #284 / #287).\n\nOBSERVATIONAL ONLY. This module records what happened; it must never change\nstage authorization, retry behavior, model prompts, runner selection, or gate\noutcomes. Timing code is not a process authority.\n\nDesign decisions (ratified in #287 tightening):\n- EVERY attempt persists its own lifecycle record — successful, gate-failed,\n failed, timed-out, interrupted. Dropping expensive failures would bias\n ADAPT-1's baseline toward cheap success.\n- Timing categories are OBSERVED, not forced additive: wall_time is not\n model_time + tool_time when MiMo waits on a tool or relay overhead overlaps.\n Records carry the raw timestamps so reports can distinguish exclusive /\n overlapping / unmeasured-residual.\n- Records survive restart: each attempt is appended to\n /telemetry/attempts.jsonl (append-only, one JSON object per\n line) AND mirrored into the task-tree state at persist time.\n- UNKNOWN_NOT_EXPOSED is recorded verbatim when a counter (e.g. provider\n tokens) is unavailable. Estimates are never presented as measurements.","truncated":false,"loaded":[]},"title":"scripts/stage_telemetry.py","time":{"start":1788985409718,"end":1788985409733}},"id":"prt_g001a087d6fc65001ruouhiYFt","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d6caef001cSg8q8GcD"}} +{"type":"step_finish","timestamp":1788985409758,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d700c60019euAxnEWs","reason":"tool-calls","snapshot":"26531853c096af2dfe82309ceb73a4b02d46e5cb","messageID":"msg_g001a087d6caef001cSg8q8GcD","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":37657,"input":1701,"output":52,"reasoning":0,"cache":{"write":0,"read":35904}},"cost":0}} +{"type":"step_start","timestamp":1788985439040,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d7733e001Gzox42Vwa","messageID":"msg_g001a087d70101001IbzxfkcSg","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"ca9736c07a0e9e0c01d49873bc31dbeacffef858","type":"step-start"}} +{"type":"text","timestamp":1788985477733,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d77927001IvKW2wLOb","messageID":"msg_g001a087d70101001IbzxfkcSg","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"text","text":"All evidence is in hand. I'll now write the OUTWARD report to `research/issue-310/outward.json` (the prior gate-failed attempt's artifact in this same session, refreshed with this run's receipts).","time":{"start":1788985440551,"end":1788985477729}}} +{"type":"tool_use","timestamp":1788985477746,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"type":"tool","tool":"write","callID":"call_593b9072c20046c79bb4da25","state":{"status":"completed","input":{"content":"{\n \"stage\": \"OUTWARD\",\n \"issue\": 310,\n \"round\": 1,\n \"subquestion\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path — model_time/tool_time always 0\",\n \"required_source_classes\": [\"repo\"],\n \"queries\": [\n {\n \"kind\": \"QUERY\",\n \"tool\": \"Grep\",\n \"pattern\": \"model_time|tool_time\",\n \"scope\": \"whole repo\",\n \"hits\": 51,\n \"receipt\": \"matches in research/issue-310/task_tree.json, telemetry/attempts.jsonl (3 attempt records), outward.json (prior attempt), scripts/stage_telemetry.py:13,140,141,155,156, src/rune/rune_operator.py:1574, src/rune/inference_trace.py:69, docs/inference-trace.md:35, plans/2026-09-05-p0-07-inference-trace.md:78,120,158, canary_p0_07.py, and 5 test files (test_p0_07_inference_trace.py, test_p1_01/02/03/06)\"\n },\n {\n \"kind\": \"QUERY\",\n \"tool\": \"Grep\",\n \"pattern\": \"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start\",\n \"scope\": \"whole repo\",\n \"hits\": 16,\n \"receipt\": \"ALL matches confined to: scripts/stage_telemetry.py (docstring lines 24-25 + definitions 83-115), tests/test_stage_telemetry.py:62-63,77 (invocations), and research/issue-310 artifacts (prior report + events log). ZERO matches in src/ and ZERO in scripts/ outside stage_telemetry.py itself.\"\n },\n {\n \"kind\": \"QUERY\",\n \"tool\": \"Grep\",\n \"pattern\": \"StageAttempt|stage_telemetry\",\n \"scope\": \"whole repo\",\n \"hits\": 47,\n \"receipt\": \"Production importers: scripts/research_stage_executor.py:87-91 ONLY. Tests: tests/test_stage_telemetry.py. src/: zero matches (src/rune has no StageAttempt reference).\"\n },\n {\n \"kind\": \"QUERY\",\n \"tool\": \"Grep\",\n \"pattern\": \"ResearchEventRelay|elapsed\",\n \"include\": \"scripts/research_stage_executor.py\",\n \"hits\": 20,\n \"receipt\": \"5 relay invocations (lines 363-364, 585-586, 755-756, 917-918, 1072-1073, 1182-1183), each followed by relay_result['elapsed'] extraction (lines 501, 658, 811, 964, 1243, 1306, 1407); line 1215 shows a no-op relay_result with elapsed 0.0. No StageAttempt/telemetry references anywhere in the executor's stage bodies.\"\n },\n {\n \"kind\": \"SOURCE_READ\",\n \"tool\": \"Read\",\n \"target\": \"scripts/stage_telemetry.py\",\n \"lines\": \"1-221 (full)\"\n },\n {\n \"kind\": \"SOURCE_READ\",\n \"tool\": \"Read\",\n \"target\": \"scripts/research_stage_executor.py\",\n \"lines\": \"60-129\"\n },\n {\n \"kind\": \"SOURCE_READ\",\n \"tool\": \"Read\",\n \"target\": \"tests/test_stage_telemetry.py\",\n \"lines\": \"50-109\"\n }\n ],\n \"sources\": [\n {\n \"id\": \"O1\",\n \"path\": \"scripts/stage_telemetry.py\",\n \"classification\": \"PRIMARY\",\n \"locus\": \"lines 2-28 (module docstring, OBS-1 epic #284/#287), 45-79 (StageAttempt.__init__: window lists + counters), 83-115 (mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end), 119-128 (finish() force-closes unclosed windows), 132-143 (summary(): model_time_s/tool_time_s = summed window pairs, explicitly non-additive), 186-218 (TelemetryLedger append-only persist, never raises)\"\n },\n {\n \"id\": \"O2\",\n \"path\": \"scripts/research_stage_executor.py\",\n \"classification\": \"PRIMARY\",\n \"locus\": \"lines 85-101: sole production importer; constructs StageAttempt + TelemetryLedger per stage event, runs _execute_stage, then calls finish(status-mapping) + persist. NO mark_* call between construction (line 89) and finish (line 96).\"\n },\n {\n \"id\": \"O3\",\n \"path\": \"tests/test_stage_telemetry.py\",\n \"classification\": \"TEST_ONLY\",\n \"locus\": \"lines 59-71 (only invocation site of mark_model_start/end + mark_tool_start/end in the entire repo), 74-81 (unclosed-window interrupt path)\"\n },\n {\n \"id\": \"O4\",\n \"path\": \"research/issue-310/telemetry/attempts.jsonl\",\n \"classification\": \"EMPIRICAL\",\n \"locus\": \"lines 1-3: real persisted production attempt records. ISSUE stage: wall 0.1597s, model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, window_counts all 0. INWARD stage: wall 0.2246s, same zeros. Prior OUTWARD attempt (GATE_FAILED, wall 315.06s): model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0 despite 315 seconds of real model+tool work — the strongest single datum for B1.\"\n },\n {\n \"id\": \"O5\",\n \"path\": \"src/rune/inference_trace.py + src/rune/rune_operator.py\",\n \"classification\": \"DISTINCT-SCHEMA\",\n \"locus\": \"inference_trace.py:69 (InferenceTrace.model_time_ms field), rune_operator.py:1574 (model_time_ms=round(run_usage['duration_ns']/1e6, 3)) — a working, separately-persisted model-timing surface (Ollama total_duration via transport accumulators), NOT StageAttempt.model_time_s\"\n },\n {\n \"id\": \"O6\",\n \"path\": \"scripts/research_stage_executor.py (stage bodies)\",\n \"classification\": \"ADJACENT\",\n \"locus\": \"lines 363-364, 585-586, 755-756, 917-918, 1072-1073: every stage body runs the mimo worker subprocess via ResearchEventRelay and extracts only relay_result['elapsed'] (coarse wall-clock). ResearchEventRelay has no StageAttempt references — the executor/relay boundary is exactly where model/tool time accrues without being marked.\"\n }\n ],\n \"evidence\": [\n {\n \"id\": \"E1\",\n \"source\": \"O1\",\n \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence) plus counters (model_calls, tool_calls, sources_discovered, ...); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish(); categories are explicitly non-additive vs wall_time.\",\n \"locus\": \"scripts/stage_telemetry.py:59-79, 83-128, 132-143, 154-163\"\n },\n {\n \"id\": \"E2\",\n \"source\": \"O2\",\n \"claim\": \"The stage executor is the ONLY production code importing stage_telemetry; it wraps every stage with StageAttempt(...)/finish(...)/ledger.persist(...) but NEVER calls any mark_* window marker or increments any counter between construction and finish — model_time_s/tool_time_s/window_counts are therefore structurally always 0 in production.\",\n \"locus\": \"scripts/research_stage_executor.py:87-101\"\n },\n {\n \"id\": \"E3\",\n \"source\": \"O3\",\n \"claim\": \"mark_model_start/mark_model_end/mark_tool_start/mark_tool_end are invoked only in tests/test_stage_telemetry.py:62-63,77; grep over the whole repo finds zero mark_* call sites in src/ and zero in scripts/ outside stage_telemetry.py's own definitions/docstring.\",\n \"locus\": \"tests/test_stage_telemetry.py:62-63, 77\"\n },\n {\n \"id\": \"E4\",\n \"source\": \"O4\",\n \"claim\": \"Empirical confirmation in this round's own production records: all three persisted attempts (ISSUE 0.1597s, INWARD 0.2246s, OUTWARD 315.06s wall) show model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, window_counts all 0 — including the OUTWARD attempt that consumed 315 seconds of real model/tool work.\",\n \"locus\": \"research/issue-310/telemetry/attempts.jsonl:1-3\"\n },\n {\n \"id\": \"E5\",\n \"source\": \"O6\",\n \"claim\": \"The stage bodies' actual model invocation happens inside mimo worker subprocesses driven by ResearchEventRelay, which records only a coarse wall-clock 'elapsed'; relay results are folded into stage results but never into StageAttempt windows — the instrumentation gap is precisely at the executor/relay boundary.\",\n \"locus\": \"scripts/research_stage_executor.py:363-364/501, 585-586/658, 755-756/811, 917-918/964, 1072-1073/1243\"\n },\n {\n \"id\": \"E6\",\n \"source\": \"O5\",\n \"claim\": \"A similarly-named but separate timing surface exists and DOES function: InferenceTrace.model_time_ms is populated from Ollama transport usage_total_duration_ns in the operator path. This is NOT StageAttempt.model_time_s; conflation risk when reading ADAPT-1 claims about 'model_time'.\",\n \"locus\": \"src/rune/inference_trace.py:69; src/rune/rune_operator.py:1574; docs/inference-trace.md:35\"\n },\n {\n \"id\": \"E7\",\n \"source\": \"O1\",\n \"claim\": \"stage_telemetry.py self-documents the design constraint: observational only, every attempt persists its own record including failures, categories observed and non-additive. The executor's finish() status mapping (lines 96-100) covers COMPLETED/GATE_FAILED/FAILED/INTERRUPTED; TIMEOUT is defined in TERMINAL_STATUSES but not visible in the executor's mapping expression (out of B1 scope, noted as open).\",\n \"locus\": \"scripts/stage_telemetry.py:2-28, 37; scripts/research_stage_executor.py:96-100\"\n }\n ],\n \"limitations\": [\n \"Grep receipts cover all file types repo-wide for the mark_* pattern (16 hits, no include filter), so the 'test-only invocation' finding is not limited to *.py.\",\n \"Whether ResearchEventRelay itself or the mimo subprocess could emit window events that the executor drops was not traced into research_event_relay.py internals; however, grep for StageAttempt/mark_* shows the relay module never references telemetry, so no hidden call sites exist there.\",\n \"TIMEOUT terminal-status handling: defined in stage_telemetry.py TERMINAL_STATUSES and force-closed at finish(), but the executor's status mapping expression does not map TIMEOUT; whether any caller can currently produce TIMEOUT was not traced (out of B1 scope).\",\n \"The prior OUTWARD attempt's artifact (outward.json, GATE_FAILED) was overwritten by this refresh; prior attempt's query receipts are superseded by this run's, which were re-executed for real.\"\n ],\n \"contradictions\": [\n \"None found on B1 itself: static evidence (zero production call sites for mark_* markers, O2/O3) and empirical evidence (attempts.jsonl all zeros including a 315s attempt, O4) agree.\",\n \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) — same 'model_time' naming family, different pipelines; any source claiming 'model_time is populated' would be true of the former and false of the latter.\"\n ]\n}\n","file_path":"/mnt/workdrive/issue161-harness/research/issue-310/outward.json"},"output":"Wrote file successfully.","metadata":{"diagnostics":{},"diff":"Index: /mnt/workdrive/issue161-harness/research/issue-310/outward.json\n===================================================================\n--- /mnt/workdrive/issue161-harness/research/issue-310/outward.json\n+++ /mnt/workdrive/issue161-harness/research/issue-310/outward.json\n@@ -1,199 +1,151 @@\n {\n \"stage\": \"OUTWARD\",\n \"issue\": 310,\n \"round\": 1,\n- \"attempt\": 1,\n \"subquestion\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path — model_time/tool_time always 0\",\n- \"discovery\": {\n- \"queries\": [\n- {\n- \"id\": \"Q1\",\n- \"query\": \"rg 'model_time|tool_time' src/\",\n- \"tool\": \"Grep\",\n- \"receipt\": \"src/rune/rune_operator.py:1574; src/rune/inference_trace.py:69; src/rune/canary_p0_07.py:57,162,195,224 (7 files total incl. 4 tests); scripts/stage_telemetry.py:13,140,141,155,156\"\n- },\n- {\n- \"id\": \"Q2\",\n- \"query\": \"rg 'StageAttempt|TelemetryLedger' (whole repo, *.py)\",\n- \"tool\": \"Grep\",\n- \"receipt\": \"27 matches: scripts/stage_telemetry.py (self), scripts/research_stage_executor.py:87-91 (sole production importer), tests/test_stage_telemetry.py (test only)\"\n- },\n- {\n- \"id\": \"Q3\",\n- \"query\": \"rg 'mark_model|mark_tool|mark_gate|mark_persistence' scripts/\",\n- \"tool\": \"Grep\",\n- \"receipt\": \"10 matches, ALL inside scripts/stage_telemetry.py (definitions+docstring); zero call sites in scripts/; src/ has zero matches (separate grep Q1-adjacent)\"\n- },\n- {\n- \"id\": \"Q4\",\n- \"query\": \"rg 'telemetry' src/; rg 'wall_time|queue_wait|gate_time|persistence_time' src/; rg 'terminal_status|attempt_id|GATE_FAILED' src/\",\n- \"tool\": \"Grep\",\n- \"receipt\": \"src/ telemetry: only src/rune/orchestration/capability.py:24 (string literal 'telemetry' in capability list); StageAttempt-category names: 0 matches in src/\"\n- },\n- {\n- \"id\": \"Q5\",\n- \"query\": \"grep stage_telemetry importers across scripts/ src/ tests/ (bash grep)\",\n- \"tool\": \"bash grep\",\n- \"receipt\": \"only scripts/research_stage_executor.py:87; relay (scripts/research_event_relay.py) has zero telemetry references — only its own 'elapsed' wall clock\"\n- },\n- {\n- \"id\": \"Q6\",\n- \"query\": \"empirical: read research/issue-310/telemetry/attempts.jsonl (real production records, ISSUE + INWARD, 2026-09-09)\",\n- \"tool\": \"bash cat\",\n- \"receipt\": \"both records show model_time_s=0, tool_time_s=0, gate_time_s=0, persistence_time_s=0, model_calls=0, tool_calls=0, window_counts all 0, wall_time_s 0.1597/0.2246\"\n- },\n- {\n- \"id\": \"Q7\",\n- \"query\": \"grep STAGE_ARTIFACT_FILES scripts/research_task_tree.py\",\n- \"tool\": \"bash grep\",\n- \"receipt\": \"scripts/research_task_tree.py:44-53 — OUTWARD artifact file: outward.json\"\n- }\n- ],\n- \"sources_discovered\": [\n- {\n- \"id\": \"O1\",\n- \"path\": \"scripts/stage_telemetry.py\",\n- \"kind\": \"repo-module\",\n- \"source_class\": \"repo\",\n- \"classification\": \"PRIMARY — the StageAttempt telemetry module (OBS-1, epic #284/#287); defines mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end, summary() deriving model_time_s/tool_time_s from summed windows\"\n- },\n- {\n- \"id\": \"O2\",\n- \"path\": \"scripts/research_stage_executor.py\",\n- \"kind\": \"repo-module\",\n- \"source_class\": \"repo\",\n- \"classification\": \"PRIMARY — sole production importer of stage_telemetry (lines 87-91); constructs StageAttempt + TelemetryLedger per stage event, calls only finish() + persist()\"\n- },\n- {\n- \"id\": \"O3\",\n- \"path\": \"tests/test_stage_telemetry.py\",\n- \"kind\": \"repo-test\",\n- \"source_class\": \"repo\",\n- \"classification\": \"TEST_ONLY — the ONLY place mark_model_start/mark_tool_start are ever invoked (e.g. lines 62-63, 77); confirms markers are exercised exclusively in tests\"\n- },\n- {\n- \"id\": \"O4\",\n- \"path\": \"scripts/research_event_relay.py\",\n- \"kind\": \"repo-module\",\n- \"source_class\": \"repo\",\n- \"classification\": \"ADJACENT — runs the actual mimo worker subprocess for OUTWARD; carries its own wall-clock 'elapsed' but has zero telemetry/StageAttempt hooks; the layer where model/tool time actually accrues yet is unmarked\"\n- },\n- {\n- \"id\": \"O5\",\n- \"path\": \"src/rune/inference_trace.py\",\n- \"kind\": \"repo-module\",\n- \"source_class\": \"repo\",\n- \"classification\": \"DISTINCT-SCHEMA — InferenceTrace.model_time_ms (line 69) is a different telemetry line (Ollama total_duration via transport accumulators), NOT the StageAttempt model_time_s; named similarly, easily conflated\"\n- },\n- {\n- \"id\": \"O6\",\n- \"path\": \"src/rune/rune_operator.py:1561-1640\",\n- \"kind\": \"repo-module\",\n- \"source_class\": \"repo\",\n- \"classification\": \"DISTINCT-SCHEMA — writes InferenceTrace.model_time_ms = run_usage duration_ns / 1e6 (line 1574); this is the model-side timing that DOES work in Rune's operator path, unlike StageAttempt's model_time_s\"\n- },\n- {\n- \"id\": \"O7\",\n- \"path\": \"research/issue-310/telemetry/attempts.jsonl\",\n- \"kind\": \"repo-runtime-artifact\",\n- \"source_class\": \"repo\",\n- \"classification\": \"EMPIRICAL — real production attempt records for issue #310 showing model_time_s=0, tool_time_s=0 across ISSUE and INWARD stages\"\n- },\n- {\n- \"id\": \"O8\",\n- \"path\": \"scripts/research_task_tree.py:44-53\",\n- \"kind\": \"repo-module\",\n- \"source_class\": \"repo\",\n- \"classification\": \"CONTEXT — STAGE_ARTIFACT_FILES canonical stage list; confirms ISSUE/INWARD are the deterministic stages already recorded and OUTWARD artifact target is outward.json\"\n- }\n- ],\n- \"source_count\": 8\n- },\n- \"reading\": {\n- \"sources_selected\": [\"O1\", \"O2\", \"O3\", \"O4\", \"O5\", \"O6\", \"O7\"],\n- \"sources_read\": [\"O1\", \"O2\", \"O3\", \"O4\", \"O5\", \"O6\", \"O7\"],\n- \"evidence\": [\n- {\n- \"id\": \"E1\",\n- \"source\": \"O1\",\n- \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish().\",\n- \"locus\": \"scripts/stage_telemetry.py:83-128, 140-143\"\n- },\n- {\n- \"id\": \"E2\",\n- \"source\": \"O2\",\n- \"claim\": \"The executor is the ONLY production code importing stage_telemetry; it wraps every stage with StageAttempt(...)/finish(...)/ledger.persist(...) but NEVER calls any mark_* window marker between construction and finish.\",\n- \"locus\": \"scripts/research_stage_executor.py:85-101\"\n- },\n- {\n- \"id\": \"E3\",\n- \"source\": \"O3\",\n- \"claim\": \"mark_model_start/mark_model_end/mark_tool_start/mark_tool_end are invoked only in tests/test_stage_telemetry.py; no call sites exist anywhere in scripts/ outside stage_telemetry.py itself and none in src/.\",\n- \"locus\": \"tests/test_stage_telemetry.py:62-63, 77\"\n- },\n- {\n- \"id\": \"E4\",\n- \"source\": \"O7\",\n- \"claim\": \"Empirical confirmation in this very round's production records: both persisted attempts (ISSUE 0.1597s wall, INWARD 0.2246s wall) show model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, and all window_counts 0 — the markers were never invoked.\",\n- \"locus\": \"research/issue-310/telemetry/attempts.jsonl (lines 1-2)\"\n- },\n- {\n- \"id\": \"E5\",\n- \"source\": \"O1+O2\",\n- \"claim\": \"The zero-valued categories are structural, not a data-loss bug in the ledger: summary() faithfully sums empty window lists; the ledger persists whatever summary() returns (with an observational never-raise invariant). Because no marker calls exist in the executor, the sums can only be 0 on the current production path.\",\n- \"locus\": \"scripts/stage_telemetry.py:132-141, 194-206; scripts/research_stage_executor.py:87-101\"\n- },\n- {\n- \"id\": \"E6\",\n- \"source\": \"O4\",\n- \"claim\": \"The OUTWARD stage's actual model invocation happens inside a mimo subprocess driven by ResearchEventRelay, which records only a coarse wall-clock 'elapsed' and has no StageAttempt hooks — the instrumentation gap is precisely at the executor/relay boundary.\",\n- \"locus\": \"scripts/research_event_relay.py:133, 179, 185; scripts/research_stage_executor.py:363-365\"\n- },\n- {\n- \"id\": \"E7\",\n- \"source\": \"O5+O6\",\n- \"claim\": \"A similarly-named but separate timing surface exists and DOES function: InferenceTrace.model_time_ms is populated from Ollama transport usage_total_duration_ns in the operator path (src/rune/rune_operator.py:1574). This is NOT StageAttempt.model_time_s; conflation risk when reading ADAPT-1 claims.\",\n- \"locus\": \"src/rune/inference_trace.py:64-69; src/rune/rune_operator.py:1339-1367, 1569-1574\"\n- },\n- {\n- \"id\": \"E8\",\n- \"source\": \"O1\",\n- \"claim\": \"stage_telemetry.py self-documents the design constraint: observational only, every attempt persists its own record (incl. failures), categories observed and non-additive vs wall_time — the executor's finish() status mapping (COMPLETED/GATE_FAILED/FAILED/INTERRUPTED) honors the first and last of these; TIMEOUT mapping is not visible in the executor's mapping expression.\",\n- \"locus\": \"scripts/stage_telemetry.py:2-28, 37; scripts/research_stage_executor.py:96-100\"\n- }\n- ]\n- },\n- \"coverage\": {\n- \"required_classes\": [\"repo\"],\n- \"represented_classes\": [\"repo\"],\n- \"uncovered\": []\n- },\n- \"limitations\": [\n- \"All sources are repo-internal (per REQUIRED SOURCE CLASSES: repo); no external corroboration was sought or needed.\",\n- \"scripts/research_stage_executor.py was read in two capped slices (lines 1-1001 shown fully; remainder not read) — the tail (_execute_synthesis onward, ~900 lines) was not inspected; telemetry import/marker usage was exhaustively verified by grep across the whole file, so the B1 conclusion does not depend on the unread tail.\",\n- \"window_markers in relay-side subprocess events were not parsed from stage_OUTWARD.events.jsonl; the relay has no StageAttempt references (grep-verified), so no additional marker call sites could hide there.\",\n- \"Grep receipts cover *.py files; a marker invocation from a non-Python file (e.g. shell workflow) was not searched — no such workflow scripts reference stage_telemetry based on the repo-wide importer grep.\",\n- \"TIMEOUT terminal-status handling: stage_telemetry.py defines TIMEOUT in TERMINAL_STATUSES and force-closes windows on interrupt paths, but the executor's finish() expression does not map a TIMEOUT status; whether any caller can currently produce TIMEOUT was not traced further (out of B1 scope).\"\n+ \"required_source_classes\": [\"repo\"],\n+ \"queries\": [\n+ {\n+ \"kind\": \"QUERY\",\n+ \"tool\": \"Grep\",\n+ \"pattern\": \"model_time|tool_time\",\n+ \"scope\": \"whole repo\",\n+ \"hits\": 51,\n+ \"receipt\": \"matches in research/issue-310/task_tree.json, telemetry/attempts.jsonl (3 attempt records), outward.json (prior attempt), scripts/stage_telemetry.py:13,140,141,155,156, src/rune/rune_operator.py:1574, src/rune/inference_trace.py:69, docs/inference-trace.md:35, plans/2026-09-05-p0-07-inference-trace.md:78,120,158, canary_p0_07.py, and 5 test files (test_p0_07_inference_trace.py, test_p1_01/02/03/06)\"\n+ },\n+ {\n+ \"kind\": \"QUERY\",\n+ \"tool\": \"Grep\",\n+ \"pattern\": \"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start\",\n+ \"scope\": \"whole repo\",\n+ \"hits\": 16,\n+ \"receipt\": \"ALL matches confined to: scripts/stage_telemetry.py (docstring lines 24-25 + definitions 83-115), tests/test_stage_telemetry.py:62-63,77 (invocations), and research/issue-310 artifacts (prior report + events log). ZERO matches in src/ and ZERO in scripts/ outside stage_telemetry.py itself.\"\n+ },\n+ {\n+ \"kind\": \"QUERY\",\n+ \"tool\": \"Grep\",\n+ \"pattern\": \"StageAttempt|stage_telemetry\",\n+ \"scope\": \"whole repo\",\n+ \"hits\": 47,\n+ \"receipt\": \"Production importers: scripts/research_stage_executor.py:87-91 ONLY. Tests: tests/test_stage_telemetry.py. src/: zero matches (src/rune has no StageAttempt reference).\"\n+ },\n+ {\n+ \"kind\": \"QUERY\",\n+ \"tool\": \"Grep\",\n+ \"pattern\": \"ResearchEventRelay|elapsed\",\n+ \"include\": \"scripts/research_stage_executor.py\",\n+ \"hits\": 20,\n+ \"receipt\": \"5 relay invocations (lines 363-364, 585-586, 755-756, 917-918, 1072-1073, 1182-1183), each followed by relay_result['elapsed'] extraction (lines 501, 658, 811, 964, 1243, 1306, 1407); line 1215 shows a no-op relay_result with elapsed 0.0. No StageAttempt/telemetry references anywhere in the executor's stage bodies.\"\n+ },\n+ {\n+ \"kind\": \"SOURCE_READ\",\n+ \"tool\": \"Read\",\n+ \"target\": \"scripts/stage_telemetry.py\",\n+ \"lines\": \"1-221 (full)\"\n+ },\n+ {\n+ \"kind\": \"SOURCE_READ\",\n+ \"tool\": \"Read\",\n+ \"target\": \"scripts/research_stage_executor.py\",\n+ \"lines\": \"60-129\"\n+ },\n+ {\n+ \"kind\": \"SOURCE_READ\",\n+ \"tool\": \"Read\",\n+ \"target\": \"tests/test_stage_telemetry.py\",\n+ \"lines\": \"50-109\"\n+ }\n ],\n- \"contradictions\": [\n- \"None found on B1 itself: static evidence (no production call sites for mark_* markers) and empirical evidence (real attempts.jsonl records with model_time_s=0/tool_time_s=0) agree.\",\n- \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (works, populated from transport accumulators) vs StageAttempt.model_time_s (always 0) — same 'model_time' family, different pipelines; a source citing 'model_time is populated' would be true of the former and false of the latter.\"\n+ \"sources\": [\n+ {\n+ \"id\": \"O1\",\n+ \"path\": \"scripts/stage_telemetry.py\",\n+ \"classification\": \"PRIMARY\",\n+ \"locus\": \"lines 2-28 (module docstring, OBS-1 epic #284/#287), 45-79 (StageAttempt.__init__: window lists + counters), 83-115 (mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end), 119-128 (finish() force-closes unclosed windows), 132-143 (summary(): model_time_s/tool_time_s = summed window pairs, explicitly non-additive), 186-218 (TelemetryLedger append-only persist, never raises)\"\n+ },\n+ {\n+ \"id\": \"O2\",\n+ \"path\": \"scripts/research_stage_executor.py\",\n+ \"classification\": \"PRIMARY\",\n+ \"locus\": \"lines 85-101: sole production importer; constructs StageAttempt + TelemetryLedger per stage event, runs _execute_stage, then calls finish(status-mapping) + persist. NO mark_* call between construction (line 89) and finish (line 96).\"\n+ },\n+ {\n+ \"id\": \"O3\",\n+ \"path\": \"tests/test_stage_telemetry.py\",\n+ \"classification\": \"TEST_ONLY\",\n+ \"locus\": \"lines 59-71 (only invocation site of mark_model_start/end + mark_tool_start/end in the entire repo), 74-81 (unclosed-window interrupt path)\"\n+ },\n+ {\n+ \"id\": \"O4\",\n+ \"path\": \"research/issue-310/telemetry/attempts.jsonl\",\n+ \"classification\": \"EMPIRICAL\",\n+ \"locus\": \"lines 1-3: real persisted production attempt records. ISSUE stage: wall 0.1597s, model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, window_counts all 0. INWARD stage: wall 0.2246s, same zeros. Prior OUTWARD attempt (GATE_FAILED, wall 315.06s): model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0 despite 315 seconds of real model+tool work — the strongest single datum for B1.\"\n+ },\n+ {\n+ \"id\": \"O5\",\n+ \"path\": \"src/rune/inference_trace.py + src/rune/rune_operator.py\",\n+ \"classification\": \"DISTINCT-SCHEMA\",\n+ \"locus\": \"inference_trace.py:69 (InferenceTrace.model_time_ms field), rune_operator.py:1574 (model_time_ms=round(run_usage['duration_ns']/1e6, 3)) — a working, separately-persisted model-timing surface (Ollama total_duration via transport accumulators), NOT StageAttempt.model_time_s\"\n+ },\n+ {\n+ \"id\": \"O6\",\n+ \"path\": \"scripts/research_stage_executor.py (stage bodies)\",\n+ \"classification\": \"ADJACENT\",\n+ \"locus\": \"lines 363-364, 585-586, 755-756, 917-918, 1072-1073: every stage body runs the mimo worker subprocess via ResearchEventRelay and extracts only relay_result['elapsed'] (coarse wall-clock). ResearchEventRelay has no StageAttempt references — the executor/relay boundary is exactly where model/tool time accrues without being marked.\"\n+ }\n ],\n- \"tool_receipts\": [\n- {\"kind\": \"QUERY\", \"tool\": \"memory search\", \"query\": \"ADAPT-1 telemetry model_time tool_time issue 310\", \"result\": \"10 BM25 hits; cross-project context on OBS-1/#287/#282\"},\n- {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"model_time|tool_time\", \"scope\": \"src/\", \"hits\": 6},\n- {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"StageAttempt\", \"scope\": \"src/\", \"hits\": 0},\n- {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"model_time|tool_time\", \"scope\": \"repo *.py\", \"hits\": 22},\n- {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"stage_telemetry|StageAttempt|mark_model_start|mark_tool_start|TelemetryLedger\", \"scope\": \"repo *.py\", \"hits\": 27},\n- {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"mark_model|mark_tool|mark_gate|mark_persistence\", \"scope\": \"scripts/\", \"hits\": 10 (all definitional)},\n- {\"kind\": \"QUERY\", \"tool\": \"bash grep\", \"pattern\": \"stage_telemetry importers\", \"scope\": \"scripts/ src/ tests/\", \"result\": \"1 importer\"},\n- {\"kind\": \"SOURCE_READ\", \"tool\": \"Read\", \"target\": \"scripts/stage_telemetry.py\", \"lines\": \"1-221 (full)\"},\n- {\"kind\": \"SOURCE_READ\", \"tool\": \"Read\", \"target\": \"scripts/research_stage_executor.py\", \"lines\": \"1-1001 (capped slice; tail unread)\"},\n- {\"kind\": \"SOURCE_READ\", \"tool\": \"Read\", \"target\": \"src/rune/inference_trace.py\", \"lines\": \"1-120\"},\n- {\"kind\": \"SOURCE_READ\", \"tool\": \"Read\", \"target\": \"src/rune/rune_operator.py\", \"lines\": \"1330-1419, 1420-1619, 1620-1709\"},\n- {\"kind\": \"SOURCE_READ\", \"tool\": \"bash cat\", \"target\": \"research/issue-310/telemetry/attempts.jsonl\", \"result\": \"2 records, ISSUE + INWARD\"},\n- {\"kind\": \"QUERY\", \"tool\": \"bash grep\", \"pattern\": \"STAGE_ARTIFACT_FILES\", \"scope\": \"scripts/research_task_tree.py\", \"result\": \"lines 44-53\"}\n+ \"evidence\": [\n+ {\n+ \"id\": \"E1\",\n+ \"source\": \"O1\",\n+ \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence) plus counters (model_calls, tool_calls, sources_discovered, ...); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish(); categories are explicitly non-additive vs wall_time.\",\n+ \"locus\": \"scripts/stage_telemetry.py:59-79, 83-128, 132-143, 154-163\"\n+ },\n+ {\n+ \"id\": \"E2\",\n+ \"source\": \"O2\",\n+ \"claim\": \"The stage executor is the ONLY production code importing stage_telemetry; it wraps every stage with StageAttempt(...)/finish(...)/ledger.persist(...) but NEVER calls any mark_* window marker or increments any counter between construction and finish — model_time_s/tool_time_s/window_counts are therefore structurally always 0 in production.\",\n+ \"locus\": \"scripts/research_stage_executor.py:87-101\"\n+ },\n+ {\n+ \"id\": \"E3\",\n+ \"source\": \"O3\",\n+ \"claim\": \"mark_model_start/mark_model_end/mark_tool_start/mark_tool_end are invoked only in tests/test_stage_telemetry.py:62-63,77; grep over the whole repo finds zero mark_* call sites in src/ and zero in scripts/ outside stage_telemetry.py's own definitions/docstring.\",\n+ \"locus\": \"tests/test_stage_telemetry.py:62-63, 77\"\n+ },\n+ {\n+ \"id\": \"E4\",\n+ \"source\": \"O4\",\n+ \"claim\": \"Empirical confirmation in this round's own production records: all three persisted attempts (ISSUE 0.1597s, INWARD 0.2246s, OUTWARD 315.06s wall) show model_time_s=0, tool_time_s=0, model_calls=0, tool_calls=0, window_counts all 0 — including the OUTWARD attempt that consumed 315 seconds of real model/tool work.\",\n+ \"locus\": \"research/issue-310/telemetry/attempts.jsonl:1-3\"\n+ },\n+ {\n+ \"id\": \"E5\",\n+ \"source\": \"O6\",\n+ \"claim\": \"The stage bodies' actual model invocation happens inside mimo worker subprocesses driven by ResearchEventRelay, which records only a coarse wall-clock 'elapsed'; relay results are folded into stage results but never into StageAttempt windows — the instrumentation gap is precisely at the executor/relay boundary.\",\n+ \"locus\": \"scripts/research_stage_executor.py:363-364/501, 585-586/658, 755-756/811, 917-918/964, 1072-1073/1243\"\n+ },\n+ {\n+ \"id\": \"E6\",\n+ \"source\": \"O5\",\n+ \"claim\": \"A similarly-named but separate timing surface exists and DOES function: InferenceTrace.model_time_ms is populated from Ollama transport usage_total_duration_ns in the operator path. This is NOT StageAttempt.model_time_s; conflation risk when reading ADAPT-1 claims about 'model_time'.\",\n+ \"locus\": \"src/rune/inference_trace.py:69; src/rune/rune_operator.py:1574; docs/inference-trace.md:35\"\n+ },\n+ {\n+ \"id\": \"E7\",\n+ \"source\": \"O1\",\n+ \"claim\": \"stage_telemetry.py self-documents the design constraint: observational only, every attempt persists its own record including failures, categories observed and non-additive. The executor's finish() status mapping (lines 96-100) covers COMPLETED/GATE_FAILED/FAILED/INTERRUPTED; TIMEOUT is defined in TERMINAL_STATUSES but not visible in the executor's mapping expression (out of B1 scope, noted as open).\",\n+ \"locus\": \"scripts/stage_telemetry.py:2-28, 37; scripts/research_stage_executor.py:96-100\"\n+ }\n ],\n- \"model\": \"Verboo Code/glm-5.3-flash\",\n- \"notes\": \"Evidence requirement for the subquestion is satisfied; stopping research here per DO-NOT-continue rule. No synthesis of a Rune solution performed; no code modified.\"\n+ \"limitations\": [\n+ \"Grep receipts cover all file types repo-wide for the mark_* pattern (16 hits, no include filter), so the 'test-only invocation' finding is not limited to *.py.\",\n+ \"Whether ResearchEventRelay itself or the mimo subprocess could emit window events that the executor drops was not traced into research_event_relay.py internals; however, grep for StageAttempt/mark_* shows the relay module never references telemetry, so no hidden call sites exist there.\",\n+ \"TIMEOUT terminal-status handling: defined in stage_telemetry.py TERMINAL_STATUSES and force-closed at finish(), but the executor's status mapping expression does not map TIMEOUT; whether any caller can currently produce TIMEOUT was not traced (out of B1 scope).\",\n+ \"The prior OUTWARD attempt's artifact (outward.json, GATE_FAILED) was overwritten by this refresh; prior attempt's query receipts are superseded by this run's, which were re-executed for real.\"\n+ ],\n+ \"contradictions\": [\n+ \"None found on B1 itself: static evidence (zero production call sites for mark_* markers, O2/O3) and empirical evidence (attempts.jsonl all zeros including a 315s attempt, O4) agree.\",\n+ \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) — same 'model_time' naming family, different pipelines; any source claiming 'model_time is populated' would be true of the former and false of the latter.\"\n+ ]\n }\n","filepath":"/mnt/workdrive/issue161-harness/research/issue-310/outward.json","exists":true,"truncated":false},"title":"research/issue-310/outward.json","time":{"start":1788985477730,"end":1788985477744}},"id":"prt_g001a087d77934001mq5Q5tMJ5","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","messageID":"msg_g001a087d70101001IbzxfkcSg"}} +{"type":"step_finish","timestamp":1788985477771,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d80a720013msdVG7g3","reason":"tool-calls","snapshot":"326fd198d2d0329fd93e2eaca58d4a18d49fd05d","messageID":"msg_g001a087d70101001IbzxfkcSg","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":44503,"input":3121,"output":3750,"reasoning":0,"cache":{"write":0,"read":37632}},"cost":0}} +{"type":"step_start","timestamp":1788985489568,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d8389e0012OASKkMX4","messageID":"msg_g001a087d80ad5001iluTkTzSf","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","snapshot":"3b320a092bc40276cce9a5f62cc9426d8d990064","type":"step-start"}} +{"type":"text","timestamp":1788985496962,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d838ff0028acOFWWud","messageID":"msg_g001a087d80ad5001iluTkTzSf","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"text","text":"OUTWARD stage complete. Report written to `research/issue-310/outward.json`.\n\n**Search receipts (all real, this run):**\n- `Grep \"model_time|tool_time\"` — 51 hits repo-wide\n- `Grep \"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start\"` — 16 hits, all confined to definitions/docstring in `scripts/stage_telemetry.py`, test invocations in `tests/test_stage_telemetry.py:62-63,77`, and prior research artifacts — **zero call sites in `src/` and zero in `scripts/` outside the module itself**\n- `Grep \"StageAttempt|stage_telemetry\"` — 47 hits; sole production importer is `scripts/research_stage_executor.py:87-91`\n- `Grep \"ResearchEventRelay|elapsed\"` in executor — 20 hits; 5 relay invocations, only `elapsed` extracted\n- Full reads: `scripts/stage_telemetry.py` (1-221), `research_stage_executor.py` (60-129), `test_stage_telemetry.py` (50-109)\n\n**Sources classified:**\n- **O1** `scripts/stage_telemetry.py` — PRIMARY (StageAttempt: 4 marker pairs, `summary()` derives `model_time_s`/`tool_time_s` from summed windows, stage_telemetry.py:83-143)\n- **O2** `scripts/research_stage_executor.py:87-101` — PRIMARY (constructs StageAttempt, never calls any `mark_*` between line 89 and `finish()` at line 96)\n- **O3** `tests/test_stage_telemetry.py` — TEST_ONLY (only invocation site in the repo)\n- **O4** `research/issue-310/telemetry/attempts.jsonl` — EMPIRICAL (all three attempts incl. the 315s OUTWARD one show `model_time_s=0`, `tool_time_s=0`, `window_counts` all 0)\n- **O5** `inference_trace.py:69` / `rune_operator.py:1574` — DISTINCT-SCHEMA (`InferenceTrace.model_time_ms` works, easily conflated with StageAttempt's zeroed fields)\n- **O6** executor stage bodies — ADJACENT (mimo subprocess via relay; only coarse `elapsed` captured)\n\n**Evidence:** 7 claims (E1–E7) extracted. Static and empirical evidence agree with no contradictions; the one near-contradiction (`model_time` populated vs. zero) is resolved as two different pipelines. Limitations noted: relay internals not traced further (grep-verified no telemetry references), TIMEOUT status mapping untraced (out of B1 scope). No synthesis, no solution design, no code modified.","time":{"start":1788985489663,"end":1788985496961}}} +{"type":"step_finish","timestamp":1788985496985,"sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","part":{"id":"prt_g001a087d85582001R1qSQSSAm","reason":"stop","snapshot":"1b3c3384c228d08690710336c599ea6de26144c8","messageID":"msg_g001a087d80ad5001iluTkTzSf","sessionID":"ses_-ffe5f782a30b9ffeXheAHMyyt","type":"step-finish","tokens":{"total":44247,"input":2852,"output":627,"reasoning":0,"cache":{"write":0,"read":40768}},"cost":0}} diff --git a/research/issue-310/task_tree.json b/research/issue-310/task_tree.json new file mode 100644 index 00000000..4351f727 --- /dev/null +++ b/research/issue-310/task_tree.json @@ -0,0 +1,241 @@ +{ + "issue": 310, + "round": 1, + "current_stage": "OUTWARD", + "completed_stages": [ + "ISSUE", + "INWARD" + ], + "artifacts": { + "ISSUE": { + "contract": { + "issue": 310, + "title": "ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \u2014 model_time/tool_time always 0", + "question_type": "repo-specific", + "sub_questions": [ + "Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \u2014 model_time/tool_time always 0" + ], + "required_stages": [ + "ISSUE", + "INWARD", + "SYNTHESIS", + "QUALIFY" + ], + "skipped_stages": { + "ADVERSARIAL": "No design choice requiring adversarial evaluation.", + "APPLICABILITY": "No OUTWARD research to ground against Rune.", + "INNOVATION": "STANDARD_SOLUTION_SUFFICIENT \u2014 INNOVATION is conditional, not ceremonial.", + "OUTWARD": "No external factual uncertainty identified in the issue." + }, + "evidence_classes": [ + "repo" + ], + "sufficient_evidence": "\u22652 code-path citations for INWARD; \u22652 unique URLs for OUTWARD (if required)", + "expected_artifact": "synthesis.json", + "ambiguous": false, + "needs_mimo": false, + "source": "deterministic" + }, + "deterministic": true + }, + "INWARD": { + "context": { + "keywords": [ + "adapt", + "local", + "like", + "telemetry", + "markers", + "never", + "invoked", + "production", + "path", + "model_time", + "tool_time", + "always", + "pre", + "registered", + "adapt", + "baseline", + "task", + "baseline", + "master", + "expected" + ], + "umbrella_matches": [ + { + "umbrella": "memory", + "module": "drift_compression", + "keyword": "pre" + }, + { + "umbrella": "perception", + "module": "context_pressure", + "keyword": "pre" + }, + { + "umbrella": "perception", + "module": "auto_tasks", + "keyword": "task" + }, + { + "umbrella": "capability", + "module": "local_tool_runtime", + "keyword": "local" + }, + { + "umbrella": "inference", + "module": "response_adapter", + "keyword": "adapt" + }, + { + "umbrella": "orchestration", + "module": "task_gate", + "keyword": "task" + }, + { + "umbrella": "orchestration", + "module": "task_persist", + "keyword": "task" + } + ], + "candidate_files": [ + "src/rune/rune_operator.py", + "src/rune/elastic_cognition.py", + "src/rune/growth_delta.py", + "src/rune/persona_gate.py", + "src/rune/rune_tui.py", + "src/rune/canary_p0_07.py", + "src/rune/canary_p1_02.py", + "src/rune/canary_p1_06.py", + "src/rune/capability_broker.py", + "src/rune/claim_audit.py", + "src/rune/contracts.py", + "src/rune/memory_selector.py", + "src/rune/memory_store.py", + "src/rune/model_routing.py", + "src/rune/canary_context_broker.py" + ], + "candidate_tests": [ + "tests/test_p1_01_capability_authority.py", + "tests/test_p1_06_model_routing.py", + "tests/test_pipeline_integration.py", + "tests/test_p1_03_memory_selection.py", + "tests/test_p0_07_inference_trace.py", + "tests/test_p1_02_capsule_authority.py", + "tests/test_retrieval_benchmark.py", + "tests/test_adapt9_artifact_authority.py", + "tests/test_failure_auto_filer_task2.py", + "tests/test_research_sweep.py" + ], + "define_contract": { + "issue": 310, + "title": "ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \u2014 model_time/tool_time always 0", + "question_type": "repo-specific", + "sub_questions": [ + "Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \u2014 model_time/tool_time always 0" + ], + "required_stages": [ + "ISSUE", + "INWARD", + "SYNTHESIS", + "QUALIFY" + ], + "skipped_stages": { + "ADVERSARIAL": "No design choice requiring adversarial evaluation.", + "APPLICABILITY": "No OUTWARD research to ground against Rune.", + "INNOVATION": "STANDARD_SOLUTION_SUFFICIENT \u2014 INNOVATION is conditional, not ceremonial.", + "OUTWARD": "No external factual uncertainty identified in the issue." + }, + "evidence_classes": [ + "repo" + ], + "sufficient_evidence": "\u22652 code-path citations for INWARD; \u22652 unique URLs for OUTWARD (if required)", + "expected_artifact": "synthesis.json", + "ambiguous": false, + "needs_mimo": false, + "source": "deterministic" + }, + "mimo_needed": false + }, + "deterministic": true, + "claims": [ + { + "id": "I1", + "claim": "Relevant file: src/rune/rune_operator.py", + "evidence": [ + "src/rune/rune_operator.py" + ], + "status": "CURRENT" + }, + { + "id": "I2", + "claim": "Relevant file: src/rune/elastic_cognition.py", + "evidence": [ + "src/rune/elastic_cognition.py" + ], + "status": "CURRENT" + }, + { + "id": "I3", + "claim": "Relevant file: src/rune/growth_delta.py", + "evidence": [ + "src/rune/growth_delta.py" + ], + "status": "CURRENT" + }, + { + "id": "I4", + "claim": "Relevant file: src/rune/persona_gate.py", + "evidence": [ + "src/rune/persona_gate.py" + ], + "status": "CURRENT" + }, + { + "id": "I5", + "claim": "Relevant file: src/rune/rune_tui.py", + "evidence": [ + "src/rune/rune_tui.py" + ], + "status": "CURRENT" + } + ] + } + }, + "blocking_unknowns": [], + "status": "ACTIVE", + "stage_history": [ + { + "stage": "ISSUE", + "round": 1, + "completed_at": "auto", + "artifact_keys": [ + "contract", + "deterministic" + ] + }, + { + "stage": "INWARD", + "round": 1, + "completed_at": "auto", + "artifact_keys": [ + "context", + "deterministic", + "claims" + ] + }, + { + "stage": "OUTWARD", + "round": 1, + "failed": true, + "reason": "OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \u2014 search snippets alone do not count as evidence" + }, + { + "stage": "OUTWARD", + "round": 1, + "failed": true, + "reason": "OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \u2014 search snippets alone do not count as evidence" + } + ] +} \ No newline at end of file diff --git a/research/issue-310/telemetry/attempts.jsonl b/research/issue-310/telemetry/attempts.jsonl new file mode 100644 index 00000000..a8e339d8 --- /dev/null +++ b/research/issue-310/telemetry/attempts.jsonl @@ -0,0 +1,4 @@ +{"attempt_id": "issue-310-ISSUE-1-a1", "event_id": "issue-310-ISSUE-1", "stage": "ISSUE", "issue": 310, "process_id": "research-310-r1", "started_at": "2026-09-09T20:15:09.949030+00:00", "finished_at": "2026-09-09T20:15:10.108621+00:00", "terminal_status": "COMPLETED", "wall_time_s": 0.1597, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-310-INWARD-1-a1", "event_id": "issue-310-INWARD-1", "stage": "INWARD", "issue": 310, "process_id": "research-310-r1", "started_at": "2026-09-09T20:15:34.792311+00:00", "finished_at": "2026-09-09T20:15:35.016831+00:00", "terminal_status": "COMPLETED", "wall_time_s": 0.2246, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-310-OUTWARD-1-a1", "event_id": "issue-310-OUTWARD-1", "stage": "OUTWARD", "issue": 310, "process_id": "research-310-r1", "started_at": "2026-09-09T20:16:01.999099+00:00", "finished_at": "2026-09-09T20:21:17.061293+00:00", "terminal_status": "GATE_FAILED", "wall_time_s": 315.0622, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-310-OUTWARD-2-a2", "event_id": "issue-310-OUTWARD-2", "stage": "OUTWARD", "issue": 310, "process_id": "research-310-r1", "started_at": "2026-09-09T20:22:10.283396+00:00", "finished_at": "2026-09-09T20:27:01.337302+00:00", "terminal_status": "GATE_FAILED", "wall_time_s": 291.0539, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} diff --git a/research/issue-311/.consumed_issue-311-INWARD-1 b/research/issue-311/.consumed_issue-311-INWARD-1 new file mode 100644 index 00000000..cd25560b --- /dev/null +++ b/research/issue-311/.consumed_issue-311-INWARD-1 @@ -0,0 +1 @@ +consumed at 1788985684.7973979 \ No newline at end of file diff --git a/research/issue-311/.consumed_issue-311-ISSUE-1 b/research/issue-311/.consumed_issue-311-ISSUE-1 new file mode 100644 index 00000000..cc45b9e8 --- /dev/null +++ b/research/issue-311/.consumed_issue-311-ISSUE-1 @@ -0,0 +1 @@ +consumed at 1788985684.6077118 \ No newline at end of file diff --git a/research/issue-311/.consumed_issue-311-OUTWARD-1 b/research/issue-311/.consumed_issue-311-OUTWARD-1 new file mode 100644 index 00000000..b0b0b2d2 --- /dev/null +++ b/research/issue-311/.consumed_issue-311-OUTWARD-1 @@ -0,0 +1 @@ +consumed at 1788985705.7648733 \ No newline at end of file diff --git a/research/issue-311/outward.json b/research/issue-311/outward.json new file mode 100644 index 00000000..3113188f --- /dev/null +++ b/research/issue-311/outward.json @@ -0,0 +1,192 @@ +{ + "stage": "OUTWARD", + "issue": 311, + "round": 1, + "attempt": 1, + "subquestion": "Address: ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)", + "required_source_classes": ["repo", "external"], + "queries": [ + { + "kind": "SEARCH", + "tool": "webfetch DuckDuckGo HTML SERP", + "query": "architecture report generated from import graph \"no hand-maintained\" dependency diagram CI", + "result": "0 results (search engine returned empty SERP)", + "receipt": "webfetch 200, SERP text 'No results found for ...' — recorded as-is; topic covered by narrower queries below" + }, + { + "kind": "SEARCH", + "tool": "webfetch DuckDuckGo HTML SERP", + "query": "import-linter enforce architecture layers python contract", + "result": "10 hits", + "receipt": "webfetch 200; hits included import-linter.readthedocs.io contract_types/layers/, github.com/seddonym/import-linter, pypi.org/project/import-linter (published 2026-09-04), roman.pt/posts/python-architecture-linter/, matthiaseg.github.io ADR-0001" + }, + { + "kind": "SEARCH", + "tool": "webfetch DuckDuckGo HTML SERP", + "query": "pytest architectural fitness functions dependency rules plugin", + "result": "10 hits", + "receipt": "webfetch 200; hits included github.com/LukasNiessen/ArchUnitPython, pypi.org/project/PyTestArch/, deepwiki.com/jwbargsten/pytest-archon, andi-john-dev.de/blog/python-architecture-rules-with-pytestarch (2026-04-28)" + }, + { + "kind": "SEARCH", + "tool": "webfetch DuckDuckGo HTML SERP", + "query": "madge dependency-cruiser visualize module dependencies report", + "result": "10 hits", + "receipt": "webfetch 200; hits included github.com/pahen/madge, www.npmjs.com/package/dependency-cruiser (v18.1.1), densitylabs.io madge walkthrough" + }, + { + "kind": "SEARCH", + "tool": "webfetch DuckDuckGo HTML SERP", + "query": "vulture dead code python static analysis unreferenced", + "result": "10 hits", + "receipt": "webfetch 200; hits included github.com/jendrikseipp/vulture, pypi.org/project/vulture/, stackoverflow.com/questions/693070" + }, + { + "kind": "SEARCH", + "tool": "webfetch DuckDuckGo HTML SERP", + "query": "ArchUnit architecture test freeze violations report", + "result": "10 hits", + "receipt": "webfetch 200; hits included www.archunit.org/userguide/html/000_Index.html, github.com/TNG/ArchUnit, deepwiki Freezing Architecture Rules page, codecentric.de ArchUnit-in-practice" + } + ], + "sources": [ + { + "id": "O1", + "url": "https://github.com/seddonym/import-linter", + "classification": "OFFICIAL_DOCS (project README, Python)", + "selected_for_reading": true, + "read_receipt": "webfetch 200 of https://raw.githubusercontent.com/seddonym/import-linter/main/README.md (first attempt .../README.rst returned 404; retried .md successfully)" + }, + { + "id": "O2", + "url": "https://import-linter.readthedocs.io/en/stable/contract_types/layers/", + "classification": "OFFICIAL_DOCS (Layers contract type)", + "selected_for_reading": false, + "read_receipt": "SERP snippet only (README read instead covers the tool's contract surface)" + }, + { + "id": "O3", + "url": "https://pypi.org/project/PyTestArch/", + "classification": "REGISTRY (Python pytest architecture-rule library)", + "selected_for_reading": false, + "read_receipt": "SERP snippet only" + }, + { + "id": "O4", + "url": "https://github.com/sverweij/dependency-cruiser", + "classification": "OFFICIAL_DOCS (project README, JS/TS)", + "selected_for_reading": true, + "read_receipt": "webfetch 200 of https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/README.md" + }, + { + "id": "O5", + "url": "https://github.com/jendrikseipp/vulture", + "classification": "OFFICIAL_DOCS (project README, Python dead-code finder)", + "selected_for_reading": true, + "read_receipt": "webfetch 200 of https://raw.githubusercontent.com/jendrikseipp/vulture/main/README.md" + }, + { + "id": "O6", + "url": "https://www.archunit.org/userguide/html/000_Index.html", + "classification": "OFFICIAL_DOCS (ArchUnit 1.5.0 user guide, Java)", + "selected_for_reading": true, + "read_receipt": "webfetch 200 (large page; head+tail captured, middle truncated at ~1114 lines — frozen-rules section read via SERP/DeepWiki snippet corroboration)" + }, + { + "id": "R1", + "path": "/mnt/workdrive/rune/config/v1_authority_map.json", + "classification": "REPO_ARTIFACT (authority map data source named by issue #311)", + "selected_for_reading": true, + "read_receipt": "file exists (26,538 bytes, mtime 2026-09-08); top-level keys: schema_version, status ('decision-record-not-migration'), epic (#16), issue (#17), frozen_reference (tag v0.42.0, commit 98b8a70...), v1_branch, invariant, dispositions, responsibilities" + }, + { + "id": "R2", + "path": "/mnt/workdrive/rune/scripts/gen_prd_state.py", + "classification": "REPO_PATTERN (generated-state report, the 'PRD state generator' pattern cited by issue #311)", + "selected_for_reading": true, + "read_receipt": "grep receipts: def version() :29, def git_head() :35, def pipeline_state() :44, def tui_state() :100 ('### TUI-adjacent subsystems (impact map)' :115), def regenerate(path, new_state) :129, def main :146; module docstring: PRD 'current state' sections are GENERATED from governed sources (config/umbrellas.json, pipeline/TUI state) while doctrine sections are hand-written" + }, + { + "id": "R3", + "path": "/mnt/workdrive/rune/src/rune/orchestration/benchmark.py", + "classification": "REPO_PATTERN (orchestration reporting pattern cited by issue #311)", + "selected_for_reading": true, + "read_receipt": "grep receipts: def fingerprint() :22, def run_batches() :40, def p95() :51, def evaluate_p95_regression() :57" + }, + { + "id": "R4", + "path": "/mnt/workdrive/rune/scripts/ (whole dir) + repo-wide grep", + "classification": "REPO_PATTERN (claimed 'module scan / walkman pattern' named by issue #311)", + "selected_for_reading": true, + "read_receipt": "ls scripts/ = 19 files (auto_bump_pr.py, capture_failure.py, gen_prd_state.py, lint_ratchet.py, type_check_ratchet.py, umbrella_impact.py, ...); repo-wide grep -i 'walkman' over src/, scripts/, docs/ AND harness repo returned ZERO hits; closest existing module-scan tooling = capture_failure.py (fixture capture) and umbrella_impact.py; no AST-based import-graph scanner exists in scripts/" + } + ], + "evidence": [ + { + "id": "E1", + "source": "O1", + "claim": "import-linter (Python, BSD-2, actively published 2026-09-04) analyzes import relationships between modules and checks them against user-declared contracts; it additionally 'provides a browser-based user interface for exploring the architecture of any Python package' — i.e. mature tooling treats the import graph as the machine-checkable source of architecture truth, with contracts (layers/forbidden/independence) as the declarative layer on top.", + "locus": "README.md; corroborated by SERP hit roman.pt/posts/python-architecture-linter/ ('in a config file, you define so-called contracts')" + }, + { + "id": "E2", + "source": "O4", + "claim": "dependency-cruiser 'runs through the dependencies in any JS/TS project and validates them against your own rules, reports violated rules in text (for your builds) and in graphics (for your eyeballs)'; default init rules include orphans and circular dependencies; output types include dot, mermaid, json, csv, html, text; it 'uses itself to check on itself in its own build process'. Key shape for #158: ONE engine emits BOTH a machine-consumable validation report AND a human status/graph view from the same scan.", + "locus": "README.md sections 'What's this do?', 'Validate things', 'Report them', package.json depcruise script reference" + }, + { + "id": "E3", + "source": "O5", + "claim": "vulture finds unused Python code via AST analysis, assigning confidence values per code type (unreachable code 100%, imports 90%, attributes/functions/variables 60%) and supports --min-confidence, whitelists, and programmatic use (v.scavenge([...]); v.get_unused_code() -> Item objects). Directly relevant to #158's 'identifies a deliberately disconnected test subsystem as not-live': liveness claims about unreferenced modules are inherently probabilistic and need a confidence/whitelist mechanism — 'code that is only called implicitly may be reported as unused' (dynamic dispatch, registries, hooks).", + "locus": "README.md sections 'Types of unused code' (confidence table), 'Handling false positives', 'How does it work?', 'Integrations' (programmatic API)" + }, + { + "id": "E4", + "source": "O6", + "claim": "ArchUnit's established pattern: import the code graph (ClassFileImporter), evaluate rules as tests, report violations with exact class+line; FreezingArchRule stores known violations in a frozen file so legacy violations don't block adoption and auto-shrinks as violations are fixed (regression protection). The 'architecture as test' pattern is language-portable (Python ports ArchUnitPython, PyTestArch, pytest-archon exist — O3/SERP). #158's report table maps onto this: Defined/Live-caller/Causal-path columns are exactly the predicates such rules evaluate.", + "locus": "ArchUnit User Guide 1.5.0 sections 3.2, 5.2, 7.1; freeze mechanism corroborated by DeepWiki 'Freezing Architecture Rules' + codecentric.de ('Thanks to the freeze function violations can be fixed step by step')" + }, + { + "id": "E5", + "source": "R1", + "claim": "config/v1_authority_map.json exists as named by issue #311 and already carries governed, machine-readable authority data (schema_version, status='decision-record-not-migration', invariant, dispositions, responsibilities) — a report generator can consume it directly as the authority-coverage column source rather than inventing a new registry.", + "locus": "file top-level keys; 26.5 KB" + }, + { + "id": "E6", + "source": "R2", + "claim": "Rune already has an in-house generated-truth-report precedent with the exact epistemic split #158 needs: gen_prd_state.py regenerates the PRDs' 'current state' sections FROM governed sources (config/umbrellas.json, pipeline/TUI state) while leaving doctrine sections hand-written, and it includes a subsystem 'impact map' section; regenerate() handles the write-back idempotently.", + "locus": "gen_prd_state.py docstring + def pipeline_state :44, def tui_state :100, def regenerate :129" + }, + { + "id": "E7", + "source": "R3", + "claim": "The orchestration reporting pattern cited by the issue exists: orchestration/benchmark.py provides fingerprint() (run provenance), p95(), and evaluate_p95_regression() — i.e. deterministic evaluation helpers a wiring report's CI/test integration can reuse for its own 'regenerable deterministically' acceptance criterion.", + "locus": "benchmark.py:22,40,51,57" + }, + { + "id": "E8", + "source": "R4", + "claim": "CONTRADICTION with issue #311's dependency list: the claimed 'module scan (walkman pattern exists)' does NOT exist — zero grep hits for 'walkman' anywhere in /mnt/workdrive/rune (src/, scripts/, docs/, all file types) or the harness repo. The dependency named in the issue is stale or refers to an unmerged/renamed artifact; the design contract must not assume it.", + "locus": "grep receipts recorded in R4" + } + ], + "limitations": [ + "External evidence is tooling-pattern evidence (import-graph linters, dead-code finders, architecture-test frameworks); no source describes a 'wiring-status report' combining import graph + authority-map coverage in one table — the specific #158 artifact shape appears to be novel to Rune, so cross-tool comparison covers components, not the composite.", + "ArchUnit user guide (O6) was fetched but the page is very large; the freeze section was captured only via head/tail + SERP/DeepWiki corroboration, not the full in-page text.", + "Liveness detection evidence (O5) is Python-static-analysis-specific; Rune's actual dynamic dispatch surface (hook_engine, capability_broker, plugin-style registration) may need registry-aware liveness rules none of the read sources directly address — flagged as an unresolved design input for later stages.", + "O2 (import-linter layers contract docs), O3 (PyTestArch), and the madge README were discovered and classified but NOT read in full; their evidence here is SERP-snippet-level and should not be cited beyond classification.", + "Repo-side receipts (R1–R4) are grep/ls/file-level, not full-file reads; line numbers come from grep -n output." + ], + "contradictions": [ + "E8 vs issue #311 text: issue names 'module scan (walkman pattern exists)' as a known dependency; repo-wide grep finds no 'walkman' anything. Either the issue text is stale or the pattern lives outside this working tree.", + "None otherwise: O1/O4/O6/E5–E7 are mutually consistent — machine-generated architecture truth from the import graph + registries, with declarative contracts/rules layered on, is the converged industry pattern." + ], + "supporting_evidence_count": 8, + "unresolved_questions": [ + "What defines 'live caller / causal path' for Rune subsystems that are wired via registries, hooks, or dynamic dispatch (where static import analysis alone would falsely report not-live — E3's whitelist/confidence problem)?", + "Should the report validate (test-style, ArchUnit/dependency-cruiser pattern, E2/E4) or only report (status view), or both from one engine as dependency-cruiser does?", + "Is v1_authority_map.json (R1) the single authority-coverage source, or do other registries (config/umbrellas.json per E6) also belong in the coverage column?" + ], + "next_stage_hint": "ADVERSARIAL should attack: (a) static-import-only liveness vs Rune's dynamic wiring (false not-live reports — E3 false-positive evidence); (b) the stale 'walkman' dependency claim (E8) if the design contract leans on it; (c) determinism of any report that embeds timestamps/graph layout." +} diff --git a/research/issue-311/stage_OUTWARD.events.jsonl b/research/issue-311/stage_OUTWARD.events.jsonl new file mode 100644 index 00000000..82b802fe --- /dev/null +++ b/research/issue-311/stage_OUTWARD.events.jsonl @@ -0,0 +1,144 @@ +{"type":"step_start","timestamp":1788985732140,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dbec290016m7oXJxCB","messageID":"msg_g001a087db8b68001avauCXHQy","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"c5f1c2427b2cc9fbed67dd93328f67dfe1b394ec","type":"step-start"}} +{"type":"text","timestamp":1788985734050,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dbed550020POtxEwJb","messageID":"msg_g001a087db8b68001avauCXHQy","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"I'll start with the required skill search and check project memory for context on Issue #311 / #158 / ADAPT-1 B2.","time":{"start":1788985732437,"end":1788985734047}}} +{"type":"tool_use","timestamp":1788985734054,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"skill_search","callID":"call_bf531b3ec76742568f640e7e","state":{"status":"completed","input":{"query":"Discover and classify candidate external web sources for a research question about a wiring-status/architecture-truth report generator feature; perform real web searches, record source IDs and fetch receipts, extract evidence claims, produce an evidence report. Audience: engineering research pipeline."},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"firecrawl-search\",\n \"name\": \"firecrawl-search\",\n \"score\": 0.6224,\n \"reason\": \"The skill description matches these query terms: discover, web, source, research, about, real.\"\n },\n {\n \"skill_id\": \"super-research\",\n \"name\": \"super-research\",\n \"score\": 0.5326,\n \"reason\": \"The skill description matches these query terms: candidate, external, source, research, question, about, real, record, evidence, produce.\"\n },\n {\n \"skill_id\": \"firecrawl\",\n \"name\": \"firecrawl\",\n \"score\": 0.4664,\n \"reason\": \"The skill description matches these query terms: external, web, research, real, fetch.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788985734049,"end":1788985734052}},"id":"prt_g001a087dbed5b001TFQBYAgIb","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087db8b68001avauCXHQy"}} +{"type":"tool_use","timestamp":1788985734237,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"memory","callID":"call_3b97b2ea418743a185f89ebd","state":{"status":"completed","input":{"operation":"search","query":"ADAPT-1 wiring-status architecture-truth report generator"},"output":"Found 10 matches (BM25-ranked, best first).\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\nIf you need the FULL body (snippets are truncated), Read the path.\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint-pipeline-stage-specs.md\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 10.663\n...25. **<>**: update `~/Desktop/RUNE_RESEARCHPROCESS_RATIFICATION_<>.md` with the canonical responsibility table (PI/GITEA, DESKTOP RUNNER, RUNE BACKEND, MIMO CODE, HENXELS/GATES, GITEA TRANSITION LOGIC); <> EXPERIMENTAL until one real...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint.md\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 10.011\n...<<1>>), file channel primary (→<>-9). REMOVE: nothing further.\n\nClosing doctrine (verbatim, cumulative): \"A REPLACEMENT IS NOT COMPLETE WHILE THE OLD AUTHORITY CAN STILL QUIETLY RUN.\" / \"ADDITION WITHOUT RETIREMENT CREATES GHOST <>...\n\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY.md\nScope: projects/global, Type: memory, Score: 8.266\n...verified live; <> at ~/Desktop/RUNE_<>_INTEGRITY_<>.md. P0-B/C/D/E DEFERRED.\n- **FEATURE-CLOBBER 3 INSTANCES (2026-09-08):** #210 tui_validator, #212 umbrella-<>, #242 canary-contract...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL/tui_build_ledger.md\nScope: sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL, Type: free, Score: 7.056\n...Source of <> for build progress._\n_TUI is at: /mnt/workdrive/rune/src/rune/_\n_<> is mandatory: standalone scripts = not done._\n\n## Pre-build Checklist\n- [x] Deep research complete (27 papers, source links...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL/build_ledger.md\nScope: sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL, Type: free, Score: 6.976\n...Source of <> for build progress._\n_Backups at: ~/.config/opencode/backups/drift-build-20260824/_\n_<> is mandatory: standalone scripts = not done._\n\n## Pre-build Checklist\n- [x] Deep research complete (27 papers, source links...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f98e46b46ffe7fdgz483s/checkpoint.md\nScope: sessions/ses_-ffe5f98e46b46ffe7fdgz483s, Type: checkpoint, Score: 6.925\n...New issues: **#155** (P1 activation/execution trace — #35 prerequisite), **#156** (P1 mutation guards), **#157** (P1 failure→fixture), **#158** (P2 <>-<> <>), **#159** (P3 adaptive concepts — Nick's design; gated on #33...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_fdfd9d8feffeq4KFcaRIVNGF9N/checkpoint.md\nScope: sessions/ses_fdfd9d8feffeq4KFcaRIVNGF9N, Type: checkpoint, Score: 6.890\n...Microkernel plugin <> — minimal dispatch engine, self-registering plugins, hook-based events, persona authority\n_Generated by checkpoint writer; structure preserved across updates. Edit only content under italic instructions._\n\n## §<<1>> Active intent\n_User...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a28a2ecffeEs822F6Ir/checkpoint.md\nScope: sessions/ses_-ffe5f7a28a2ecffeEs822F6Ir, Type: checkpoint, Score: 6.736\n...md ## <> decisions when proven cross-session-durable._\n\n- (pending artifact) Working disposition for innovation.json, based on verified evidence: conclusion likely <>, with candidates — C5 as <> (NEP-001 Phase-<<1>> AS...\n\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY-environment-doctrine-2026-09-06.md\nScope: projects/global, Type: memory, Score: 6.482\n...181 + #182 (GitHub code-search ADOPT/<>/BORROW/BUILD) + #183 (typed temporal semantics); #161 carries two addenda (canonical-authority contract + extended bake-off dimensions, \"simplest <> that measurably wins\"). All P3 — low...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fdce6835cffezTlUd3Fwd/checkpoint.md\nScope: sessions/ses_-ffe5fdce6835cffezTlUd3Fwd, Type: checkpoint, Score: 6.448\n...Pushed v0.8.0 + v0.8.<<1>>.\n\n### Self-iteration loop — COMPLETE\n\n- ✅ All pins closed. Cache <>. Abstraction pipeline. RuneBench 30→36. Witness 7 signals. Pushed v0.9.0–v0.10.<<1>>.\n\n### Rune...\n","metadata":{"count":10,"truncated":false},"title":"Memory search: 10 results","time":{"start":1788985734051,"end":1788985734236}},"id":"prt_g001a087dbf0ae0011zE7e7Exp","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087db8b68001avauCXHQy"}} +{"type":"tool_use","timestamp":1788985734329,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"memory","callID":"call_c2fe52d584af4f7a92071325","state":{"status":"completed","input":{"operation":"search","query":"issue 311 outward research stage"},"output":"Found 10 matches (BM25-ranked, best first).\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\nIf you need the FULL body (snippets are truncated), Read the path.\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c/checkpoint.md\nScope: sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c, Type: checkpoint, Score: 7.781\n...<> artifact must be recorded here when done.\n- `/mnt/workdrive/<>-<>/<>/<>-263/runs/` — per-run directory for <> artifacts.\n- `/mnt/workdrive/<>-<>/prompts/<>-stages/<>.md` — the...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 7.718\n...List with one-line purpose._\n\n- `/mnt/workdrive/<>-<>/<>/<>-161/task_tree.json` — round state; <>+INWARD done, <> next\n- `/mnt/workdrive/<>-<>/<>/<>-263/<>.json` — artifact...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR/checkpoint.md\nScope: sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR, Type: checkpoint, Score: 7.633\n...<>/<>-310/<>.json (or <>-named <> artifact) ending `StageCompleted: <>`.\n- Untracked evidence receipts already on disk: <>/<>-310/<>_<>.events.jsonl, <>/<>-310/telemetry/attempts.jsonl...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 7.616\n...Read `<>/<>-163/task_tree.json` → current_<> <>, completed [<>, INWARD], status ACTIVE, no artifacts beyond <> (deterministic contract; evidence bar: ≥2 unique URLs for <>) and INWARD (auto keyword...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB/checkpoint.md\nScope: sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB, Type: checkpoint, Score: 7.581\n...Read `<>/<>-163/task_tree.json`: confirmed <> state (<>, INWARD, <> complete; APPLICABILITY next) and INWARD claims I1–I5.\n\nNOT yet done: writing `<>/<>-163/applicability.json`. The classification...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint-rp2-correction-265-outward-specs.md\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 7.513\n...runner_hostname, runner_name, runner_labels, execution_class (desktop-heavy), <>, process_id, <>_id, <>_process_id, timestamp. Example: runner_name = rune-ci-desktop, execution_class = desktop-heavy, <> = <>.\n- Negative...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f826d4b3affeptScH2iSt/notes.md\nScope: sessions/ses_-ffe5f826d4b3affeptScH2iSt, Type: free, Score: 7.442\n...NO DEFINE <>. The Gitea <> IS the <> contract. <> quality is checked deterministically.\n2. NEW PIPELINE: <> → INWARD → <> → APPLICABILITY → INNOVATION? → ADVERSARIAL → SYNTHESIS → QUALIFY → PROCEED/REFINE\n3. <> is ONE...\n\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 7.406\n...at `/mnt/workdrive/issue161-harness/<>/<>-163/task_tree.json` (NOT the <>-<> repo — that older path doesn't apply to new rounds). <> accepted (attempt 2; first failed with...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cedfc3cffe4Jz3Ie6ag/checkpoint.md\nScope: sessions/ses_-ffe5f7cedfc3cffe4Jz3Ie6ag, Type: checkpoint, Score: 7.386\n...Located <> artifacts on disk: /mnt/workdrive/<>-<>/<>/<>-262/ contains <>_reading.json (SOURCES S1-S11, CLAIMS O1-O19, mapping_to_repo, limitations, next_<>_hint — read in full...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint-handoff-digest.md\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 7.385\n...Governing frame — conflicts with earlier <>-<> designs resolve in its favor.\n\n1. **Pipeline**: GITEA <> → INWARD → <> → APPLICABILITY → INNOVATION?(optional) → ADVERSARIAL → SYNTHESIS → QUALIFY → (PROCEED → IMPLEMENT/CODE → VERIFY/ACCEPT → OUTCOME/LEARNING | REFINE...\n","metadata":{"count":10,"truncated":false},"title":"Memory search: 10 results","time":{"start":1788985734053,"end":1788985734326}},"id":"prt_g001a087dbf1f2001vR1UU1sNG","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087db8b68001avauCXHQy"}} +{"type":"step_finish","timestamp":1788985734574,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dbf4b9001AriYI524V","reason":"tool-calls","snapshot":"77fb7d2e8dfd0495295e9fa70ff928280a5a3db7","messageID":"msg_g001a087db8b68001avauCXHQy","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":28770,"input":28481,"output":289,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}} +{"type":"step_start","timestamp":1788985750105,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc3257001jfMld0iBH","messageID":"msg_g001a087dbf6c6001i6M8UQKAI","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"5e592a19cde024e2c753be072782084c66fae5c1","type":"step-start"}} +{"type":"text","timestamp":1788985751630,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc32e40019FMJz2r1r","messageID":"msg_g001a087dbf6c6001i6M8UQKAI","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"This is a Rune research-pipeline OUTWARD stage for Issue #311. Let me gather the issue context and locate the research workspace.","time":{"start":1788985750244,"end":1788985751627}}} +{"type":"tool_use","timestamp":1788985751647,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"read","callID":"call_c157a6320a9d4679bb199f2f","state":{"status":"completed","input":{"file_path":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md"},"output":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\nfile\n\n1: # Memory spillover: pipeline history & completed early-stage artifacts\n2: _Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\n3: \n4: ## Entries\n5: \n6: - Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\n7: - Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \"the research question\" without restating it — locate the question file before classifying.\n8: - Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \"OUTWARD_DISCOVERY executor\"); distinct from the subject repo `/mnt/workdrive/rune/`.\n9: - OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \"discover URLs, classify sources, don't read full sources\" stage contract.\n10: - OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\n11: - Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\n12: - Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \"skipped\" while the unified stage still requires \"≥2 unique URLs\".\n13: - Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \"vs Qdrant vs graph backends\" part of the question is comparative research, not existing code.\n14: - Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\n15: - Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\n16: - Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\"PROCEED\"|\"REFINE_SYNTHESIS\") + `findings` (+ optional `reopen`).\n17: \n18: - Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\n19: \n20: - Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \"temporal+lexical query layer over graph.sqlite\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).\n21: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT, single merged candidate INN-1 \"temporal+lexical query layer over graph.sqlite\" (composition_of C5+C6+existing C4 schema; novelty=compositional; zero schema/backend change; embedding tier deliberately omitted). kill_test = two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t; lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files on this machine: `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py`; the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json carries NO `StageCompleted:` line — convention for JSON artifacts unconfirmed. (An earlier near-duplicate INNOVATION entry further below predates this one; both agree on facts.) (1) add_edge (graph_memory.py:200-204) writes the SAME now_iso() into valid_from AND learned_at and always NULL valid_until — the \"bi-temporal\" schema has only ever carried one effective time axis; an as-of filter degenerates to transaction-time-only unless a distinct valid-time source is added. (2) Supersession is triple-local: only identical (subj_id, pred, obj_id) active edges are superseded (:184-186); reworded contradictions across predicates/entities co-exist active. (3) Only idx_edges_subj/idx_edges_obj exist (:117-118) — no index serves temporal or status filters. (4) query() BFS traverses outgoing edges only (:257-267); incoming edges appended at depth 0 but never expanded. (5) Entity resolution is ... (line truncated to 2000 chars)\n22: \n23: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT with single merged candidate INN-1 \"temporal+lexical query layer over graph.sqlite\" (composition_of C5+C6+existing C4 schema; novelty=compositional; no schema/backend change; embedding tier deliberately omitted). kill_test has two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t (negligible superseded accumulation); lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files exist on this machine — `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py` — the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json does NOT end with a `StageCompleted:` line; convention for JSON artifacts unconfirmed.\n\n(End of file - total 23 lines)\n","metadata":{"preview":"# Memory spillover: pipeline history & completed early-stage artifacts\n_Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\n\n## Entries\n\n- Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\n- Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \"the research question\" without restating it — locate the question file before classifying.\n- Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \"OUTWARD_DISCOVERY executor\"); distinct from the subject repo `/mnt/workdrive/rune/`.\n- OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \"discover URLs, classify sources, don't read full sources\" stage contract.\n- OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\n- Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\n- Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \"skipped\" while the unified stage still requires \"≥2 unique URLs\".\n- Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \"vs Qdrant vs graph backends\" part of the question is comparative research, not existing code.\n- Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\n- Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\n- Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\"PROCEED\"|\"REFINE_SYNTHESIS\") + `findings` (+ optional `reopen`).\n\n- Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\n\n- Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \"temporal+lexical query layer over graph.sqlite\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).","truncated":false,"loaded":[]},"title":"../../../home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md","time":{"start":1788985751630,"end":1788985751646}},"id":"prt_g001a087dc33c1001QD0AgRV1T","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dbf6c6001i6M8UQKAI"}} +{"type":"tool_use","timestamp":1788985751699,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_2e03be522f0540e4b85f95f4","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/ 2>/dev/null; ls /mnt/workdrive/research-stage/research/ 2>/dev/null | tail -30","description":"List workdrive and research issue dirs"},"output":"Bottles\ncoding\nissue161-harness\nMusic Journalism\nMusic Production\np1-03\np122\np149\np150\np151\np192\nprobot\nprun\nReaper Projects\nRed Giant\nresearch-stage\nrobot-fix\nrune\nscarlett config\nSteamLibrary\nStudio One Projects\nVideo Projects\nwine-ni\nbatch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-245-research.md\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\n","metadata":{"output":"Bottles\ncoding\nissue161-harness\nMusic Journalism\nMusic Production\np1-03\np122\np149\np150\np151\np192\nprobot\nprun\nReaper Projects\nRed Giant\nresearch-stage\nrobot-fix\nrune\nscarlett config\nSteamLibrary\nStudio One Projects\nVideo Projects\nwine-ni\nbatch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-245-research.md\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\n","exit":0,"description":"List workdrive and research issue dirs","truncated":false},"title":"List workdrive and research issue dirs","time":{"start":1788985751681,"end":1788985751697}},"id":"prt_g001a087dc350a001Wh0K1iHtM","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dbf6c6001i6M8UQKAI"}} +{"type":"tool_use","timestamp":1788985751720,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"memory","callID":"call_31709926511642aca5896e54","state":{"status":"completed","input":{"operation":"search","query":"issue-311"},"output":"Found 10 matches (BM25-ranked, best first).\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\nIf you need the FULL body (snippets are truncated), Read the path.\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB/checkpoint.md\nScope: sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB, Type: checkpoint, Score: 1.450\n...Read `research/<>-163/task_tree.json`: confirmed stage state (<>, INWARD, OUTWARD complete; APPLICABILITY next) and INWARD claims I1–I5.\n\nNOT yet done: writing `research/<>-163/applicability.json`. The classification...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 1.443\n...Read `research/<>-163/task_tree.json` → current_stage OUTWARD, completed [<>, INWARD], status ACTIVE, no artifacts beyond <> (deterministic contract; evidence bar: ≥2 unique URLs for OUTWARD) and INWARD (auto keyword...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fbceff801ffehugGFc5Es/tasks/T6/progress.md\nScope: sessions/ses_-ffe5fbceff801ffehugGFc5Es, Type: progress, Score: 1.439\n...Traced `create_<>`, `create_<>_comment`, `create_pr`, `work_on_<>`, `_create_pr_from_worktree`, `generate_daily_report`, documentation generation, `format_<>_plain`, and CLI entry points; found raw body pass-through...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 1.438\n...List with one-line purpose._\n\n- `/mnt/workdrive/research-stage/research/<>-161/task_tree.json` — round state; <>+INWARD done, OUTWARD next\n- `/mnt/workdrive/research-stage/research/<>-263/outward.json` — artifact...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR/checkpoint.md\nScope: sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR, Type: checkpoint, Score: 1.431\n...research/<>-310/outward.json (or stage-named OUTWARD artifact) ending `StageCompleted: OUTWARD`.\n- Untracked evidence receipts already on disk: research/<>-310/stage_OUTWARD.events.jsonl, research/<>-310/telemetry/attempts.jsonl...\n\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 1.430\n...pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for <>-262 / <>-161 / <>-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY/checkpoint.md\nScope: sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY, Type: checkpoint, Score: 1.425\n...SYNTHESIS\n> <>: #161\n>\n> The epistemic process is COMPLETE. Research, applicability, and adversarial\n> attack are settled. Survivors, required mitigations, and falsification tests\n> are ACCEPTED CONSTRAINTS — not suggestions.\"\n\n(Paraphrased: SYNTHESIS-stage dispatch for <>...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a21f590ffen7XSOiEUr/checkpoint.md\nScope: sessions/ses_-ffe5f7a21f590ffen7XSOiEUr, Type: checkpoint, Score: 1.419\n...write the structured dossier as JSON to research/<>-163/adversarial.json ...\\\"...\"\n\n(Paraphrased: ADVERSARIAL-stage dispatch for <>-163 round 1. Route INNOVATION_WARRANTED; attack the leading candidates C4 (DIRECTLY_APPLICABLE — bi...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c/checkpoint.md\nScope: sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c, Type: checkpoint, Score: 1.419\n...<>-263 task_tree.json has DEFINE + INWARD completed; OUTWARD artifact pending (must land in `research/<>-263/` and be referenced by task_tree.json when the main agent finalizes it).\n\n## §10...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f79e20228ffeq2voHZxjl/checkpoint.md\nScope: sessions/ses_-ffe5f79e20228ffeq2voHZxjl, Type: checkpoint, Score: 1.416\n...QUALIFY gate for <>-163 round 2 — 5 implementation-readiness checks, verdict JSON to `research/<>-163/qualify.json` with `outcome` (\"PROCEED\" | \"REFINE_SYNTHESIS\") + `findings`; optional sparing `reopen` for genuinely new blocking...\n","metadata":{"count":10,"truncated":false},"title":"Memory search: 10 results","time":{"start":1788985751629,"end":1788985751718}},"id":"prt_g001a087dc32ea00146tzI6hWQ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dbf6c6001i6M8UQKAI"}} +{"type":"step_finish","timestamp":1788985751746,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc38a8001GmcESKhW0","reason":"tool-calls","snapshot":"3bcfd7880a5b576689b0a8e5dd3e654522319798","messageID":"msg_g001a087dbf6c6001i6M8UQKAI","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":32144,"input":14319,"output":225,"reasoning":0,"cache":{"write":0,"read":17600}},"cost":0}} +{"type":"step_start","timestamp":1788985760823,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc5c35001L9P5AtOvy","messageID":"msg_g001a087dc38e50014nr7JHrJm","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"20d5050daf9ac7d207b5d70574b5e91def251278","type":"step-start"}} +{"type":"text","timestamp":1788985761486,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc5cc90020MCy9u15D","messageID":"msg_g001a087dc38e50014nr7JHrJm","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"The harness repo at `/mnt/workdrive/issue161-harness` is where newer rounds live. Let me check for issue-311 there.","time":{"start":1788985760969,"end":1788985761484}}} +{"type":"tool_use","timestamp":1788985761510,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_70928ccc59944f7bb4e2499b","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/issue161-harness/research/ 2>/dev/null; ls /mnt/workdrive/issue161-harness/research/issue-311/ 2>/dev/null","description":"Check harness repo research dirs for issue-311"},"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nissue-310\nissue-311\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n","metadata":{"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nissue-310\nissue-311\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n","exit":0,"description":"Check harness repo research dirs for issue-311","truncated":false},"title":"Check harness repo research dirs for issue-311","time":{"start":1788985761507,"end":1788985761509}},"id":"prt_g001a087dc5cce001YC3DGSyYe","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dc38e50014nr7JHrJm"}} +{"type":"step_finish","timestamp":1788985761568,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc5ee600128yO42VJd","reason":"tool-calls","snapshot":"e2b2842c991f69610cfd48e190e5a5a0de813d12","messageID":"msg_g001a087dc38e50014nr7JHrJm","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":36707,"input":4631,"output":140,"reasoning":0,"cache":{"write":0,"read":31936}},"cost":0}} +{"type":"step_start","timestamp":1788985771881,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc8765001dRJ3AnAxr","messageID":"msg_g001a087dc5f7a001V4Yw8KDjz","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"ce241fa16e7c8e51bd854585dec5e249e795a332","type":"step-start"}} +{"type":"tool_use","timestamp":1788985773648,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_3342790509534b0ab070ffec","state":{"status":"completed","input":{"command":"cat /mnt/workdrive/issue161-harness/research/issue-311/task_tree.json; echo ---; cat /mnt/workdrive/issue161-harness/research/issue-311/stage_OUTWARD.events.jsonl 2>/dev/null | tail -20; echo ---; cat /mnt/workdrive/issue161-harness/research/issue-311/stage_OUTWARD.mimo.err.log 2>/dev/null | tail -10; echo ---; ls /mnt/workdrive/issue161-harness/research/issue-311/telemetry/","description":"Read issue-311 task tree and stage events"},"output":"...output truncated...\n\nFull output saved to: /home/nick/.local/share/mimocode/tool-output/tool_g001a087dc8e4b001uU5xUK5iD\n\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"deterministic\": true\n },\n \"INWARD\": {\n \"context\": {\n \"keywords\": [\n \"adapt\",\n \"standard\",\n \"like\",\n \"wiring\",\n \"status\",\n \"architecture\",\n \"truth\",\n \"report\",\n \"generator\",\n \"pre\",\n \"registered\",\n \"adapt\",\n \"baseline\",\n \"task\",\n \"implements\",\n \"open\",\n \"issue\",\n \"baseline\",\n \"master\",\n \"expected\"\n ],\n \"umbrella_matches\": [\n {\n \"umbrella\": \"memory\",\n \"module\": \"drift_compression\",\n \"keyword\": \"pre\"\n },\n {\n \"umbrella\": \"perception\",\n \"module\": \"context_pressure\",\n \"keyword\": \"pre\"\n },\n {\n \"umbrella\": \"perception\",\n \"module\": \"auto_tasks\",\n \"keyword\": \"task\"\n },\n {\n \"umbrella\": \"inference\",\n \"module\": \"standardized_providers\",\n \"keyword\": \"standard\"\n },\n {\n \"umbrella\": \"inference\",\n \"module\": \"response_adapter\",\n \"keyword\": \"adapt\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"task_gate\",\n \"keyword\": \"task\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"task_persist\",\n \"keyword\": \"task\"\n }\n ],\n \"candidate_files\": [\n \"src/rune/rune_operator.py\",\n \"src/rune/elastic_cognition.py\",\n \"src/rune/rune_tui.py\",\n \"src/rune/contracts.py\",\n \"src/rune/memory_store.py\",\n \"src/rune/claim_audit.py\",\n \"src/rune/curator.py\",\n \"src/rune/growth_delta.py\",\n \"src/rune/safety_override.py\",\n \"src/rune/voice_hybrid.py\",\n \"src/rune/canary_full_system.py\",\n \"src/rune/canary_native.py\",\n \"src/rune/capability_broker.py\",\n \"src/rune/failure_auto_filer.py\",\n \"src/rune/hook_engine.py\"\n ],\n \"candidate_tests\": [\n \"tests/test_pipeline_integration.py\",\n \"tests/test_research_task_tree.py\",\n \"tests/test_adapt9_artifact_authority.py\",\n \"tests/test_failure_auto_filer_task2.py\",\n \"tests/test_merge_robot.py\",\n \"tests/test_p1_01_capability_authority.py\",\n \"tests/test_p1_06_model_routing.py\",\n \"tests/test_synthesis_executor.py\",\n \"tests/test_applicability_executor.py\",\n \"tests/test_p0_07_inference_trace.py\"\n ],\n \"define_contract\": {\n \"issue\": 311,\n \"title\": \"ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"mimo_needed\": false\n },\n \"deterministic\": true,\n \"claims\": [\n {\n \"id\": \"I1\",\n \"claim\": \"Relevant file: src/rune/rune_operator.py\",\n \"evidence\": [\n \"src/rune/rune_operator.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I2\",\n \"claim\": \"Relevant file: src/rune/elastic_cognition.py\",\n \"evidence\": [\n \"src/rune/elastic_cognition.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I3\",\n \"claim\": \"Relevant file: src/rune/rune_tui.py\",\n \"evidence\": [\n \"src/rune/rune_tui.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I4\",\n \"claim\": \"Relevant file: src/rune/contracts.py\",\n \"evidence\": [\n \"src/rune/contracts.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I5\",\n \"claim\": \"Relevant file: src/rune/memory_store.py\",\n \"evidence\": [\n \"src/rune/memory_store.py\"\n ],\n \"status\": \"CURRENT\"\n }\n ]\n }\n },\n \"blocking_unknowns\": [],\n \"status\": \"ACTIVE\",\n \"stage_history\": [\n {\n \"stage\": \"ISSUE\",\n \"round\": 1,\n \"completed_at\": \"auto\",\n \"artifact_keys\": [\n \"contract\",\n \"deterministic\"\n ]\n },\n {\n \"stage\": \"INWARD\",\n \"round\": 1,\n \"completed_at\": \"auto\",\n \"artifact_keys\": [\n \"context\",\n \"deterministic\",\n \"claims\"\n ]\n }\n ]\n}---\n{\"type\":\"step_start\",\"timestamp\":1788985732140,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dbec290016m7oXJxCB\",\"messageID\":\"msg_g001a087db8b68001avauCXHQy\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"snapshot\":\"c5f1c2427b2cc9fbed67dd93328f67dfe1b394ec\",\"type\":\"step-start\"}}\n{\"type\":\"text\",\"timestamp\":1788985734050,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dbed550020POtxEwJb\",\"messageID\":\"msg_g001a087db8b68001avauCXHQy\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"text\",\"text\":\"I'll start with the required skill search and check project memory for context on Issue #311 / #158 / ADAPT-1 B2.\",\"time\":{\"start\":1788985732437,\"end\":1788985734047}}}\n{\"type\":\"tool_use\",\"timestamp\":1788985734054,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"skill_search\",\"callID\":\"call_bf531b3ec76742568f640e7e\",\"state\":{\"status\":\"completed\",\"input\":{\"query\":\"Discover and classify candidate external web sources for a research question about a wiring-status/architecture-truth report generator feature; perform real web searches, record source IDs and fetch receipts, extract evidence claims, produce an evidence report. Audience: engineering research pipeline.\"},\"output\":\"{\\n \\\"status\\\": \\\"matched\\\",\\n \\\"results\\\": [\\n {\\n \\\"skill_id\\\": \\\"firecrawl-search\\\",\\n \\\"name\\\": \\\"firecrawl-search\\\",\\n \\\"score\\\": 0.6224,\\n \\\"reason\\\": \\\"The skill description matches these query terms: discover, web, source, research, about, real.\\\"\\n },\\n {\\n \\\"skill_id\\\": \\\"super-research\\\",\\n \\\"name\\\": \\\"super-research\\\",\\n \\\"score\\\": 0.5326,\\n \\\"reason\\\": \\\"The skill description matches these query terms: candidate, external, source, research, question, about, real, record, evidence, produce.\\\"\\n },\\n {\\n \\\"skill_id\\\": \\\"firecrawl\\\",\\n \\\"name\\\": \\\"firecrawl\\\",\\n \\\"score\\\": 0.4664,\\n \\\"reason\\\": \\\"The skill description matches these query terms: external, web, research, real, fetch.\\\"\\n }\\n ],\\n \\\"loaded_skill_id\\\": null\\n}\",\"metadata\":{\"status\":\"matched\",\"loadedSkillID\":null,\"truncated\":false},\"title\":\"Found 3 skills\",\"time\":{\"start\":1788985734049,\"end\":1788985734052}},\"id\":\"prt_g001a087dbed5b001TFQBYAgIb\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087db8b68001avauCXHQy\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788985734237,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"memory\",\"callID\":\"call_3b97b2ea418743a185f89ebd\",\"state\":{\"status\":\"completed\",\"input\":{\"operation\":\"search\",\"query\":\"ADAPT-1 wiring-status architecture-truth report generator\"},\"output\":\"Found 10 matches (BM25-ranked, best first).\\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\\nIf you need the FULL body (snippets are truncated), Read the path.\\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint-pipeline-stage-specs.md\\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 10.663\\n...25. **<>**: update `~/Desktop/RUNE_RESEARCHPROCESS_RATIFICATION_<>.md` with the canonical responsibility table (PI/GITEA, DESKTOP RUNNER, RUNE BACKEND, MIMO CODE, HENXELS/GATES, GITEA TRANSITION LOGIC); <> EXPERIMENTAL until one real...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint.md\\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 10.011\\n...<<1>>), file channel primary (→<>-9). REMOVE: nothing further.\\n\\nClosing doctrine (verbatim, cumulative): \\\"A REPLACEMENT IS NOT COMPLETE WHILE THE OLD AUTHORITY CAN STILL QUIETLY RUN.\\\" / \\\"ADDITION WITHOUT RETIREMENT CREATES GHOST <>...\\n\\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY.md\\nScope: projects/global, Type: memory, Score: 8.266\\n...verified live; <> at ~/Desktop/RUNE_<>_INTEGRITY_<>.md. P0-B/C/D/E DEFERRED.\\n- **FEATURE-CLOBBER 3 INSTANCES (2026-09-08):** #210 tui_validator, #212 umbrella-<>, #242 canary-contract...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL/tui_build_ledger.md\\nScope: sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL, Type: free, Score: 7.056\\n...Source of <> for build progress._\\n_TUI is at: /mnt/workdrive/rune/src/rune/_\\n_<> is mandatory: standalone scripts = not done._\\n\\n## Pre-build Checklist\\n- [x] Deep research complete (27 papers, source links...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL/build_ledger.md\\nScope: sessions/ses_-ffe5fcf0fa7e9ffe73c5cHWhL, Type: free, Score: 6.976\\n...Source of <> for build progress._\\n_Backups at: ~/.config/opencode/backups/drift-build-20260824/_\\n_<> is mandatory: standalone scripts = not done._\\n\\n## Pre-build Checklist\\n- [x] Deep research complete (27 papers, source links...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f98e46b46ffe7fdgz483s/checkpoint.md\\nScope: sessions/ses_-ffe5f98e46b46ffe7fdgz483s, Type: checkpoint, Score: 6.925\\n...New issues: **#155** (P1 activation/execution trace — #35 prerequisite), **#156** (P1 mutation guards), **#157** (P1 failure→fixture), **#158** (P2 <>-<> <>), **#159** (P3 adaptive concepts — Nick's design; gated on #33...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_fdfd9d8feffeq4KFcaRIVNGF9N/checkpoint.md\\nScope: sessions/ses_fdfd9d8feffeq4KFcaRIVNGF9N, Type: checkpoint, Score: 6.890\\n...Microkernel plugin <> — minimal dispatch engine, self-registering plugins, hook-based events, persona authority\\n_Generated by checkpoint writer; structure preserved across updates. Edit only content under italic instructions._\\n\\n## §<<1>> Active intent\\n_User...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a28a2ecffeEs822F6Ir/checkpoint.md\\nScope: sessions/ses_-ffe5f7a28a2ecffeEs822F6Ir, Type: checkpoint, Score: 6.736\\n...md ## <> decisions when proven cross-session-durable._\\n\\n- (pending artifact) Working disposition for innovation.json, based on verified evidence: conclusion likely <>, with candidates — C5 as <> (NEP-001 Phase-<<1>> AS...\\n\\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY-environment-doctrine-2026-09-06.md\\nScope: projects/global, Type: memory, Score: 6.482\\n...181 + #182 (GitHub code-search ADOPT/<>/BORROW/BUILD) + #183 (typed temporal semantics); #161 carries two addenda (canonical-authority contract + extended bake-off dimensions, \\\"simplest <> that measurably wins\\\"). All P3 — low...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fdce6835cffezTlUd3Fwd/checkpoint.md\\nScope: sessions/ses_-ffe5fdce6835cffezTlUd3Fwd, Type: checkpoint, Score: 6.448\\n...Pushed v0.8.0 + v0.8.<<1>>.\\n\\n### Self-iteration loop — COMPLETE\\n\\n- ✅ All pins closed. Cache <>. Abstraction pipeline. RuneBench 30→36. Witness 7 signals. Pushed v0.9.0–v0.10.<<1>>.\\n\\n### Rune...\\n\",\"metadata\":{\"count\":10,\"truncated\":false},\"title\":\"Memory search: 10 results\",\"time\":{\"start\":1788985734051,\"end\":1788985734236}},\"id\":\"prt_g001a087dbf0ae0011zE7e7Exp\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087db8b68001avauCXHQy\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788985734329,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"memory\",\"callID\":\"call_c2fe52d584af4f7a92071325\",\"state\":{\"status\":\"completed\",\"input\":{\"operation\":\"search\",\"query\":\"issue 311 outward research stage\"},\"output\":\"Found 10 matches (BM25-ranked, best first).\\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\\nIf you need the FULL body (snippets are truncated), Read the path.\\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c/checkpoint.md\\nScope: sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c, Type: checkpoint, Score: 7.781\\n...<> artifact must be recorded here when done.\\n- `/mnt/workdrive/<>-<>/<>/<>-263/runs/` — per-run directory for <> artifacts.\\n- `/mnt/workdrive/<>-<>/prompts/<>-stages/<>.md` — the...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 7.718\\n...List with one-line purpose._\\n\\n- `/mnt/workdrive/<>-<>/<>/<>-161/task_tree.json` — round state; <>+INWARD done, <> next\\n- `/mnt/workdrive/<>-<>/<>/<>-263/<>.json` — artifact...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR/checkpoint.md\\nScope: sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR, Type: checkpoint, Score: 7.633\\n...<>/<>-310/<>.json (or <>-named <> artifact) ending `StageCompleted: <>`.\\n- Untracked evidence receipts already on disk: <>/<>-310/<>_<>.events.jsonl, <>/<>-310/telemetry/attempts.jsonl...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 7.616\\n...Read `<>/<>-163/task_tree.json` → current_<> <>, completed [<>, INWARD], status ACTIVE, no artifacts beyond <> (deterministic contract; evidence bar: ≥2 unique URLs for <>) and INWARD (auto keyword...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB/checkpoint.md\\nScope: sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB, Type: checkpoint, Score: 7.581\\n...Read `<>/<>-163/task_tree.json`: confirmed <> state (<>, INWARD, <> complete; APPLICABILITY next) and INWARD claims I1–I5.\\n\\nNOT yet done: writing `<>/<>-163/applicability.json`. The classification...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint-rp2-correction-265-outward-specs.md\\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 7.513\\n...runner_hostname, runner_name, runner_labels, execution_class (desktop-heavy), <>, process_id, <>_id, <>_process_id, timestamp. Example: runner_name = rune-ci-desktop, execution_class = desktop-heavy, <> = <>.\\n- Negative...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f826d4b3affeptScH2iSt/notes.md\\nScope: sessions/ses_-ffe5f826d4b3affeptScH2iSt, Type: free, Score: 7.442\\n...NO DEFINE <>. The Gitea <> IS the <> contract. <> quality is checked deterministically.\\n2. NEW PIPELINE: <> → INWARD → <> → APPLICABILITY → INNOVATION? → ADVERSARIAL → SYNTHESIS → QUALIFY → PROCEED/REFINE\\n3. <> is ONE...\\n\\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 7.406\\n...at `/mnt/workdrive/issue161-harness/<>/<>-163/task_tree.json` (NOT the <>-<> repo — that older path doesn't apply to new rounds). <> accepted (attempt 2; first failed with...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cedfc3cffe4Jz3Ie6ag/checkpoint.md\\nScope: sessions/ses_-ffe5f7cedfc3cffe4Jz3Ie6ag, Type: checkpoint, Score: 7.386\\n...Located <> artifacts on disk: /mnt/workdrive/<>-<>/<>/<>-262/ contains <>_reading.json (SOURCES S1-S11, CLAIMS O1-O19, mapping_to_repo, limitations, next_<>_hint — read in full...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD/checkpoint-handoff-digest.md\\nScope: sessions/ses_-ffe5f7cd9dc19ffeh9xe8IpyD, Type: checkpoint, Score: 7.385\\n...Governing frame — conflicts with earlier <>-<> designs resolve in its favor.\\n\\n1. **Pipeline**: GITEA <> → INWARD → <> → APPLICABILITY → INNOVATION?(optional) → ADVERSARIAL → SYNTHESIS → QUALIFY → (PROCEED → IMPLEMENT/CODE → VERIFY/ACCEPT → OUTCOME/LEARNING | REFINE...\\n\",\"metadata\":{\"count\":10,\"truncated\":false},\"title\":\"Memory search: 10 results\",\"time\":{\"start\":1788985734053,\"end\":1788985734326}},\"id\":\"prt_g001a087dbf1f2001vR1UU1sNG\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087db8b68001avauCXHQy\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788985734574,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dbf4b9001AriYI524V\",\"reason\":\"tool-calls\",\"snapshot\":\"77fb7d2e8dfd0495295e9fa70ff928280a5a3db7\",\"messageID\":\"msg_g001a087db8b68001avauCXHQy\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"step-finish\",\"tokens\":{\"total\":28770,\"input\":28481,\"output\":289,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":0}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788985750105,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc3257001jfMld0iBH\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"snapshot\":\"5e592a19cde024e2c753be072782084c66fae5c1\",\"type\":\"step-start\"}}\n{\"type\":\"text\",\"timestamp\":1788985751630,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc32e40019FMJz2r1r\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"text\",\"text\":\"This is a Rune research-pipeline OUTWARD stage for Issue #311. Let me gather the issue context and locate the research workspace.\",\"time\":{\"start\":1788985750244,\"end\":1788985751627}}}\n{\"type\":\"tool_use\",\"timestamp\":1788985751647,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"read\",\"callID\":\"call_c157a6320a9d4679bb199f2f\",\"state\":{\"status\":\"completed\",\"input\":{\"file_path\":\"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\"},\"output\":\"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\\nfile\\n\\n1: # Memory spillover: pipeline history & completed early-stage artifacts\\n2: _Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\\n3: \\n4: ## Entries\\n5: \\n6: - Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\\n7: - Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \\\"the research question\\\" without restating it — locate the question file before classifying.\\n8: - Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \\\"OUTWARD_DISCOVERY executor\\\"); distinct from the subject repo `/mnt/workdrive/rune/`.\\n9: - OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \\\"discover URLs, classify sources, don't read full sources\\\" stage contract.\\n10: - OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\\n11: - Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\\n12: - Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \\\"skipped\\\" while the unified stage still requires \\\"≥2 unique URLs\\\".\\n13: - Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \\\"vs Qdrant vs graph backends\\\" part of the question is comparative research, not existing code.\\n14: - Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\\n15: - Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\\n16: - Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\\\"PROCEED\\\"|\\\"REFINE_SYNTHESIS\\\") + `findings` (+ optional `reopen`).\\n17: \\n18: - Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\\n19: \\n20: - Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \\\"temporal+lexical query layer over graph.sqlite\\\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).\\n21: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT, single merged candidate INN-1 \\\"temporal+lexical query layer over graph.sqlite\\\" (composition_of C5+C6+existing C4 schema; novelty=compositional; zero schema/backend change; embedding tier deliberately omitted). kill_test = two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t; lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files on this machine: `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py`; the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json carries NO `StageCompleted:` line — convention for JSON artifacts unconfirmed. (An earlier near-duplicate INNOVATION entry further below predates this one; both agree on facts.) (1) add_edge (graph_memory.py:200-204) writes the SAME now_iso() into valid_from AND learned_at and always NULL valid_until — the \\\"bi-temporal\\\" schema has only ever carried one effective time axis; an as-of filter degenerates to transaction-time-only unless a distinct valid-time source is added. (2) Supersession is triple-local: only identical (subj_id, pred, obj_id) active edges are superseded (:184-186); reworded contradictions across predicates/entities co-exist active. (3) Only idx_edges_subj/idx_edges_obj exist (:117-118) — no index serves temporal or status filters. (4) query() BFS traverses outgoing edges only (:257-267); incoming edges appended at depth 0 but never expanded. (5) Entity resolution is ... (line truncated to 2000 chars)\\n22: \\n23: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT with single merged candidate INN-1 \\\"temporal+lexical query layer over graph.sqlite\\\" (composition_of C5+C6+existing C4 schema; novelty=compositional; no schema/backend change; embedding tier deliberately omitted). kill_test has two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t (negligible superseded accumulation); lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files exist on this machine — `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py` — the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json does NOT end with a `StageCompleted:` line; convention for JSON artifacts unconfirmed.\\n\\n(End of file - total 23 lines)\\n\",\"metadata\":{\"preview\":\"# Memory spillover: pipeline history & completed early-stage artifacts\\n_Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\\n\\n## Entries\\n\\n- Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\\n- Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \\\"the research question\\\" without restating it — locate the question file before classifying.\\n- Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \\\"OUTWARD_DISCOVERY executor\\\"); distinct from the subject repo `/mnt/workdrive/rune/`.\\n- OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \\\"discover URLs, classify sources, don't read full sources\\\" stage contract.\\n- OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\\n- Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\\n- Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \\\"skipped\\\" while the unified stage still requires \\\"≥2 unique URLs\\\".\\n- Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \\\"vs Qdrant vs graph backends\\\" part of the question is comparative research, not existing code.\\n- Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\\n- Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\\n- Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\\\"PROCEED\\\"|\\\"REFINE_SYNTHESIS\\\") + `findings` (+ optional `reopen`).\\n\\n- Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\\n\\n- Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \\\"temporal+lexical query layer over graph.sqlite\\\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).\",\"truncated\":false,\"loaded\":[]},\"title\":\"../../../home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\",\"time\":{\"start\":1788985751630,\"end\":1788985751646}},\"id\":\"prt_g001a087dc33c1001QD0AgRV1T\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788985751699,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_2e03be522f0540e4b85f95f4\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"ls /mnt/workdrive/ 2>/dev/null; ls /mnt/workdrive/research-stage/research/ 2>/dev/null | tail -30\",\"description\":\"List workdrive and research issue dirs\"},\"output\":\"Bottles\\ncoding\\nissue161-harness\\nMusic Journalism\\nMusic Production\\np1-03\\np122\\np149\\np150\\np151\\np192\\nprobot\\nprun\\nReaper Projects\\nRed Giant\\nresearch-stage\\nrobot-fix\\nrune\\nscarlett config\\nSteamLibrary\\nStudio One Projects\\nVideo Projects\\nwine-ni\\nbatch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-245-research.md\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\n\",\"metadata\":{\"output\":\"Bottles\\ncoding\\nissue161-harness\\nMusic Journalism\\nMusic Production\\np1-03\\np122\\np149\\np150\\np151\\np192\\nprobot\\nprun\\nReaper Projects\\nRed Giant\\nresearch-stage\\nrobot-fix\\nrune\\nscarlett config\\nSteamLibrary\\nStudio One Projects\\nVideo Projects\\nwine-ni\\nbatch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-245-research.md\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\n\",\"exit\":0,\"description\":\"List workdrive and research issue dirs\",\"truncated\":false},\"title\":\"List workdrive and research issue dirs\",\"time\":{\"start\":1788985751681,\"end\":1788985751697}},\"id\":\"prt_g001a087dc350a001Wh0K1iHtM\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788985751720,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"memory\",\"callID\":\"call_31709926511642aca5896e54\",\"state\":{\"status\":\"completed\",\"input\":{\"operation\":\"search\",\"query\":\"issue-311\"},\"output\":\"Found 10 matches (BM25-ranked, best first).\\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\\nIf you need the FULL body (snippets are truncated), Read the path.\\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB/checkpoint.md\\nScope: sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB, Type: checkpoint, Score: 1.450\\n...Read `research/<>-163/task_tree.json`: confirmed stage state (<>, INWARD, OUTWARD complete; APPLICABILITY next) and INWARD claims I1–I5.\\n\\nNOT yet done: writing `research/<>-163/applicability.json`. The classification...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 1.443\\n...Read `research/<>-163/task_tree.json` → current_stage OUTWARD, completed [<>, INWARD], status ACTIVE, no artifacts beyond <> (deterministic contract; evidence bar: ≥2 unique URLs for OUTWARD) and INWARD (auto keyword...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fbceff801ffehugGFc5Es/tasks/T6/progress.md\\nScope: sessions/ses_-ffe5fbceff801ffehugGFc5Es, Type: progress, Score: 1.439\\n...Traced `create_<>`, `create_<>_comment`, `create_pr`, `work_on_<>`, `_create_pr_from_worktree`, `generate_daily_report`, documentation generation, `format_<>_plain`, and CLI entry points; found raw body pass-through...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 1.438\\n...List with one-line purpose._\\n\\n- `/mnt/workdrive/research-stage/research/<>-161/task_tree.json` — round state; <>+INWARD done, OUTWARD next\\n- `/mnt/workdrive/research-stage/research/<>-263/outward.json` — artifact...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR/checkpoint.md\\nScope: sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR, Type: checkpoint, Score: 1.431\\n...research/<>-310/outward.json (or stage-named OUTWARD artifact) ending `StageCompleted: OUTWARD`.\\n- Untracked evidence receipts already on disk: research/<>-310/stage_OUTWARD.events.jsonl, research/<>-310/telemetry/attempts.jsonl...\\n\\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 1.430\\n...pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for <>-262 / <>-161 / <>-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY/checkpoint.md\\nScope: sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY, Type: checkpoint, Score: 1.425\\n...SYNTHESIS\\n> <>: #161\\n>\\n> The epistemic process is COMPLETE. Research, applicability, and adversarial\\n> attack are settled. Survivors, required mitigations, and falsification tests\\n> are ACCEPTED CONSTRAINTS — not suggestions.\\\"\\n\\n(Paraphrased: SYNTHESIS-stage dispatch for <>...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a21f590ffen7XSOiEUr/checkpoint.md\\nScope: sessions/ses_-ffe5f7a21f590ffen7XSOiEUr, Type: checkpoint, Score: 1.419\\n...write the structured dossier as JSON to research/<>-163/adversarial.json ...\\\\\\\"...\\\"\\n\\n(Paraphrased: ADVERSARIAL-stage dispatch for <>-163 round 1. Route INNOVATION_WARRANTED; attack the leading candidates C4 (DIRECTLY_APPLICABLE — bi...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c/checkpoint.md\\nScope: sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c, Type: checkpoint, Score: 1.419\\n...<>-263 task_tree.json has DEFINE + INWARD completed; OUTWARD artifact pending (must land in `research/<>-263/` and be referenced by task_tree.json when the main agent finalizes it).\\n\\n## §10...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f79e20228ffeq2voHZxjl/checkpoint.md\\nScope: sessions/ses_-ffe5f79e20228ffeq2voHZxjl, Type: checkpoint, Score: 1.416\\n...QUALIFY gate for <>-163 round 2 — 5 implementation-readiness checks, verdict JSON to `research/<>-163/qualify.json` with `outcome` (\\\"PROCEED\\\" | \\\"REFINE_SYNTHESIS\\\") + `findings`; optional sparing `reopen` for genuinely new blocking...\\n\",\"metadata\":{\"count\":10,\"truncated\":false},\"title\":\"Memory search: 10 results\",\"time\":{\"start\":1788985751629,\"end\":1788985751718}},\"id\":\"prt_g001a087dc32ea00146tzI6hWQ\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788985751746,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc38a8001GmcESKhW0\",\"reason\":\"tool-calls\",\"snapshot\":\"3bcfd7880a5b576689b0a8e5dd3e654522319798\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"step-finish\",\"tokens\":{\"total\":32144,\"input\":14319,\"output\":225,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":17600}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788985760823,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc5c35001L9P5AtOvy\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"snapshot\":\"20d5050daf9ac7d207b5d70574b5e91def251278\",\"type\":\"step-start\"}}\n{\"type\":\"text\",\"timestamp\":1788985761486,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc5cc90020MCy9u15D\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"text\",\"text\":\"The harness repo at `/mnt/workdrive/issue161-harness` is where newer rounds live. Let me check for issue-311 there.\",\"time\":{\"start\":1788985760969,\"end\":1788985761484}}}\n{\"type\":\"tool_use\",\"timestamp\":1788985761510,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_70928ccc59944f7bb4e2499b\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"ls /mnt/workdrive/issue161-harness/research/ 2>/dev/null; ls /mnt/workdrive/issue161-harness/research/issue-311/ 2>/dev/null\",\"description\":\"Check harness repo research dirs for issue-311\"},\"output\":\"batch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-163\\nissue-245-research.md\\nissue-256\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nissue-310\\nissue-311\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\nstage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n\",\"metadata\":{\"output\":\"batch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-163\\nissue-245-research.md\\nissue-256\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nissue-310\\nissue-311\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\nstage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n\",\"exit\":0,\"description\":\"Check harness repo research dirs for issue-311\",\"truncated\":false},\"title\":\"Check harness repo research dirs for issue-311\",\"time\":{\"start\":1788985761507,\"end\":1788985761509}},\"id\":\"prt_g001a087dc5cce001YC3DGSyYe\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788985761568,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc5ee600128yO42VJd\",\"reason\":\"tool-calls\",\"snapshot\":\"e2b2842c991f69610cfd48e190e5a5a0de813d12\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"step-finish\",\"tokens\":{\"total\":36707,\"input\":4631,\"output\":140,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":31936}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788985771881,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc8765001dRJ3AnAxr\",\"messageID\":\"msg_g001a087dc5f7a001V4Yw8KDjz\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"snapshot\":\"ce241fa16e7c8e51bd854585dec5e249e795a332\",\"type\":\"step-start\"}}\n---\n---\n","metadata":{"output":"...\n\nl/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\\n3: \\n4: ## Entries\\n5: \\n6: - Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\\n7: - Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \\\"the research question\\\" without restating it — locate the question file before classifying.\\n8: - Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \\\"OUTWARD_DISCOVERY executor\\\"); distinct from the subject repo `/mnt/workdrive/rune/`.\\n9: - OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \\\"discover URLs, classify sources, don't read full sources\\\" stage contract.\\n10: - OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\\n11: - Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\\n12: - Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \\\"skipped\\\" while the unified stage still requires \\\"≥2 unique URLs\\\".\\n13: - Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \\\"vs Qdrant vs graph backends\\\" part of the question is comparative research, not existing code.\\n14: - Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\\n15: - Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\\n16: - Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\\\"PROCEED\\\"|\\\"REFINE_SYNTHESIS\\\") + `findings` (+ optional `reopen`).\\n17: \\n18: - Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\\n19: \\n20: - Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \\\"temporal+lexical query layer over graph.sqlite\\\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).\\n21: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT, single merged candidate INN-1 \\\"temporal+lexical query layer over graph.sqlite\\\" (composition_of C5+C6+existing C4 schema; novelty=compositional; zero schema/backend change; embedding tier deliberately omitted). kill_test = two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t; lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files on this machine: `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py`; the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json carries NO `StageCompleted:` line — convention for JSON artifacts unconfirmed. (An earlier near-duplicate INNOVATION entry further below predates this one; both agree on facts.) (1) add_edge (graph_memory.py:200-204) writes the SAME now_iso() into valid_from AND learned_at and always NULL valid_until — the \\\"bi-temporal\\\" schema has only ever carried one effective time axis; an as-of filter degenerates to transaction-time-only unless a distinct valid-time source is added. (2) Supersession is triple-local: only identical (subj_id, pred, obj_id) active edges are superseded (:184-186); reworded contradictions across predicates/entities co-exist active. (3) Only idx_edges_subj/idx_edges_obj exist (:117-118) — no index serves temporal or status filters. (4) query() BFS traverses outgoing edges only (:257-267); incoming edges appended at depth 0 but never expanded. (5) Entity resolution is ... (line truncated to 2000 chars)\\n22: \\n23: - Issue-163 INNOVATION accepted (artifact `research/issue-163/innovation.json`, harness repo): conclusion ADAPT with single merged candidate INN-1 \\\"temporal+lexical query layer over graph.sqlite\\\" (composition_of C5+C6+existing C4 schema; novelty=compositional; no schema/backend change; embedding tier deliberately omitted). kill_test has two independently-falsifiable halves: temporal half dies if as-of result sets ≡ active-only sets for every practical t (negligible superseded accumulation); lexical half dies if FTS5 resolves <~30% of paraphrase/typo terms exact/alias/substring miss, or add-bulk latency regresses >~10% without compensating recall. next_stage_hint fixes the ADVERSARIAL attack surface: learned_at-as-transaction-time-proxy approximation, half-open/NULL interval semantics (E29 pitfalls), FTS5 write amplification on add-bulk. Known decoy `graph_memory.py` files exist on this machine — `/home/nick/.config/opencode/scripts/graph_memory.py` (401 lines) and `/home/nick/odysseus/memory_platform/graph_memory.py` — the issue-163 subject is `/mnt/workdrive/issue161-harness/src/rune/graph_memory.py` (416 lines). Note: innovation.json does NOT end with a `StageCompleted:` line; convention for JSON artifacts unconfirmed.\\n\\n(End of file - total 23 lines)\\n\",\"metadata\":{\"preview\":\"# Memory spillover: pipeline history & completed early-stage artifacts\\n_Extracted from MEMORY.md ## Discovered durable knowledge (token-budget extraction). Historical/completed content: pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for issue-262 / issue-161 / issue-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative issue-163 constraints (ADVERSARIAL M1–M7/T1–T6, SYNTHESIS round-1, QUALIFY) remain in MEMORY.md._\\n\\n## Entries\\n\\n- Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\\n- Research questions are filed in the Rune repo as `research/issue--research.md` (or under `/mnt/workdrive/rune/research//` subdirectories). DEFINE-stage dispatches may reference \\\"the research question\\\" without restating it — locate the question file before classifying.\\n- Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \\\"OUTWARD_DISCOVERY executor\\\"); distinct from the subject repo `/mnt/workdrive/rune/`.\\n- OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \\\"discover URLs, classify sources, don't read full sources\\\" stage contract.\\n- OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com////README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\\n- Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\\n- Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \\\"skipped\\\" while the unified stage still requires \\\"≥2 unique URLs\\\".\\n- Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \\\"vs Qdrant vs graph backends\\\" part of the question is comparative research, not existing code.\\n- Issue-262 round already researched SQLite WAL + concurrency (artifact at `research/issue-262/outward.json` + `.firecrawl/` source snapshots); don't re-research WAL in memory-backend rounds.\\n- Issue-161 round 1 (memory backend abstraction + comparative benchmark harness): ADVERSARIAL settled SURVIVES_WITH_MITIGATIONS. Survivors: sqlite-hybrid-retrieval, retrieval-quality-metrics-suite, reproducibility-contract, matched-precision-comparison-methodology, candidate-pool-vs-ranking-diagnostic, substrate-family-comparative-eval. memory-adapter-protocol REJECTED as duplicate authority — memory_selector.py's source registry (add_source()/DEFAULT_SOURCES, memory_selector.py:130-133 & 504-511, contract `(query, types, budget, min_score) -> list[dict]`) already IS the backend abstraction; do NOT resurrect it (mitigation M5). SYNTHESIS must carry mitigations M1–M10 and convert falsification tests FT1–FT7 into invariants/tests (full text in the SYNTHESIS dispatch and stage artifacts at `/mnt/workdrive/research-stage/research/issue-161/`; expected artifact `research/issue-161/synthesis.json`). Two earlier SYNTHESIS dispatch attempts produced no artifact.\\n- Issue-161 SYNTHESIS artifact (`research/issue-161/synthesis.json`) accepted and now in QUALIFY: selected design = ONE harness at `src/rune/orchestration/retrieval_benchmark.py` over the EXISTING memory_selector registry (M5), primary target memory_store.py sqlite hybrid; only production touches are recall() 'mode' observability field (M1) + parameterized probe_cap (M8, default-preserving); shared run-record schema + fingerprint() reused from orchestration/benchmark.py:22 (M10); 11-step implementation_sequence with M1 first, INV-1..13, 16 tests_required, AC-1..12, rollback = additive/leaf deletion. QUALIFY output artifact: `research/issue-161/qualify.json` with keys `outcome` (\\\"PROCEED\\\"|\\\"REFINE_SYNTHESIS\\\") + `findings` (+ optional `reopen`).\\n\\n- Issue-163 round 1 (temporal graph backend spike: Neo4j vs FalkorDB, LadybugDB experimental, Graphiti reference): stage state lives in the harness repo at `/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json` (NOT the research-stage repo — that older path doesn't apply to new rounds). OUTWARD accepted (attempt 2; first failed with 0 sources) with O1–O45 discovered and E1–E30 evidence; read set: O1 (AIMultiple independent benchmark), O2 (FalkorDB vendor benchmark), O5 (FalkorDB/benchmark metadata-only), O10 (Graphiti), O21 (LadybugDB), O24 (thedataquarry ex-Kuzu), O28 (dbdb.io), O29 (FalkorDB, SSPLv1), O38 (Neo4j temporal docs, truncated ~40KB), O41 (NEP-001 draft RFC). Key analytical facts: no source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first); vendor 345x p99 is marketing-adjacent, independent evidence = 6.7x concurrent with FalkorDB plateauing at Redis's single-threaded core; Neo4j has NO native temporal versioning (E27–E30), Graphiti's bi-temporal semantics are framework-layer (E14/E17), backend-independent; Graphiti endorses only Neo4j/FalkorDB (Kuzu driver deprecated, no LadybugDB driver; embedded option = graphiti-core[falkordblite], Python 3.12+). Next stage APPLICABILITY, expected artifact `research/issue-163/applicability.json` (keys: candidates list + route string with exact spelling STANDARD_SOLUTION_SUFFICIENT / INNOVATION_WARRANTED), dispatched as read-only stage (no code changes, no re-research, no stage advancement).\\n\\n- Issue-163 APPLICABILITY accepted (artifact `research/issue-163/applicability.json`, harness repo): candidates C1–C10 evaluated, route `INNOVATION_WARRANTED` on a NARROW front — no backend swap (C1 Neo4j / C2 FalkorDB rejected as conflicting with embedded posture, C3 LadybugDB insufficient evidence); C4 confirms Rune's edges schema (graph_memory.py:101-116) already implements Graphiti's E14 bi-temporal model 1:1. Innovation warranted only on two query-layer gaps: C5 AS-OF time-travel queries (NEP-001 E28–E30; query() filters status='active' only at graph_memory.py:254 and :275) and C6 FTS5 lexical fallback tier in query()'s entity resolver (:224-237, exact→alias→LIKE only). next_stage_hint: ADVERSARIAL attacks C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION may merge C5+C6 into one \\\"temporal+lexical query layer over graph.sqlite\\\" proposal. Code detail: graph_memory.py has an unwired `--latest-only` CLI flag (parsed, never used in query logic).\",\"truncated\":false,\"loaded\":[]},\"title\":\"../../../home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\",\"time\":{\"start\":1788985751630,\"end\":1788985751646}},\"id\":\"prt_g001a087dc33c1001QD0AgRV1T\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788985751699,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_2e03be522f0540e4b85f95f4\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"ls /mnt/workdrive/ 2>/dev/null; ls /mnt/workdrive/research-stage/research/ 2>/dev/null | tail -30\",\"description\":\"List workdrive and research issue dirs\"},\"output\":\"Bottles\\ncoding\\nissue161-harness\\nMusic Journalism\\nMusic Production\\np1-03\\np122\\np149\\np150\\np151\\np192\\nprobot\\nprun\\nReaper Projects\\nRed Giant\\nresearch-stage\\nrobot-fix\\nrune\\nscarlett config\\nSteamLibrary\\nStudio One Projects\\nVideo Projects\\nwine-ni\\nbatch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-245-research.md\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\n\",\"metadata\":{\"output\":\"Bottles\\ncoding\\nissue161-harness\\nMusic Journalism\\nMusic Production\\np1-03\\np122\\np149\\np150\\np151\\np192\\nprobot\\nprun\\nReaper Projects\\nRed Giant\\nresearch-stage\\nrobot-fix\\nrune\\nscarlett config\\nSteamLibrary\\nStudio One Projects\\nVideo Projects\\nwine-ni\\nbatch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-245-research.md\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\n\",\"exit\":0,\"description\":\"List workdrive and research issue dirs\",\"truncated\":false},\"title\":\"List workdrive and research issue dirs\",\"time\":{\"start\":1788985751681,\"end\":1788985751697}},\"id\":\"prt_g001a087dc350a001Wh0K1iHtM\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788985751720,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"memory\",\"callID\":\"call_31709926511642aca5896e54\",\"state\":{\"status\":\"completed\",\"input\":{\"operation\":\"search\",\"query\":\"issue-311\"},\"output\":\"Found 10 matches (BM25-ranked, best first).\\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\\nIf you need the FULL body (snippets are truncated), Read the path.\\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB/checkpoint.md\\nScope: sessions/ses_-ffe5f7a2fff97ffeShe3jrJEB, Type: checkpoint, Score: 1.450\\n...Read `research/<>-163/task_tree.json`: confirmed stage state (<>, INWARD, OUTWARD complete; APPLICABILITY next) and INWARD claims I1–I5.\\n\\nNOT yet done: writing `research/<>-163/applicability.json`. The classification...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1/checkpoint.md\\nScope: sessions/ses_-ffe5f7a3e2805ffeYaOOBiQE1, Type: checkpoint, Score: 1.443\\n...Read `research/<>-163/task_tree.json` → current_stage OUTWARD, completed [<>, INWARD], status ACTIVE, no artifacts beyond <> (deterministic contract; evidence bar: ≥2 unique URLs for OUTWARD) and INWARD (auto keyword...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fbceff801ffehugGFc5Es/tasks/T6/progress.md\\nScope: sessions/ses_-ffe5fbceff801ffehugGFc5Es, Type: progress, Score: 1.439\\n...Traced `create_<>`, `create_<>_comment`, `create_pr`, `work_on_<>`, `_create_pr_from_worktree`, `generate_daily_report`, documentation generation, `format_<>_plain`, and CLI entry points; found raw body pass-through...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 1.438\\n...List with one-line purpose._\\n\\n- `/mnt/workdrive/research-stage/research/<>-161/task_tree.json` — round state; <>+INWARD done, OUTWARD next\\n- `/mnt/workdrive/research-stage/research/<>-263/outward.json` — artifact...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR/checkpoint.md\\nScope: sessions/ses_-ffe5f782fcf32ffeYuOzzr9mR, Type: checkpoint, Score: 1.431\\n...research/<>-310/outward.json (or stage-named OUTWARD artifact) ending `StageCompleted: OUTWARD`.\\n- Untracked evidence receipts already on disk: research/<>-310/stage_OUTWARD.events.jsonl, research/<>-310/telemetry/attempts.jsonl...\\n\\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY-pipeline-history.md\\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 1.430\\n...pipeline conventions, stage techniques, grounding facts, and per-stage artifacts for <>-262 / <>-161 / <>-163 up through INNOVATION. Consult when working older rounds or needing source-level detail; the operative...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY/checkpoint.md\\nScope: sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY, Type: checkpoint, Score: 1.425\\n...SYNTHESIS\\n> <>: #161\\n>\\n> The epistemic process is COMPLETE. Research, applicability, and adversarial\\n> attack are settled. Survivors, required mitigations, and falsification tests\\n> are ACCEPTED CONSTRAINTS — not suggestions.\\\"\\n\\n(Paraphrased: SYNTHESIS-stage dispatch for <>...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7a21f590ffen7XSOiEUr/checkpoint.md\\nScope: sessions/ses_-ffe5f7a21f590ffen7XSOiEUr, Type: checkpoint, Score: 1.419\\n...write the structured dossier as JSON to research/<>-163/adversarial.json ...\\\\\\\"...\\\"\\n\\n(Paraphrased: ADVERSARIAL-stage dispatch for <>-163 round 1. Route INNOVATION_WARRANTED; attack the leading candidates C4 (DIRECTLY_APPLICABLE — bi...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c/checkpoint.md\\nScope: sessions/ses_-ffe5f7ce5b46cffec8hvvAn1c, Type: checkpoint, Score: 1.419\\n...<>-263 task_tree.json has DEFINE + INWARD completed; OUTWARD artifact pending (must land in `research/<>-263/` and be referenced by task_tree.json when the main agent finalizes it).\\n\\n## §10...\\n\\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f79e20228ffeq2voHZxjl/checkpoint.md\\nScope: sessions/ses_-ffe5f79e20228ffeq2voHZxjl, Type: checkpoint, Score: 1.416\\n...QUALIFY gate for <>-163 round 2 — 5 implementation-readiness checks, verdict JSON to `research/<>-163/qualify.json` with `outcome` (\\\"PROCEED\\\" | \\\"REFINE_SYNTHESIS\\\") + `findings`; optional sparing `reopen` for genuinely new blocking...\\n\",\"metadata\":{\"count\":10,\"truncated\":false},\"title\":\"Memory search: 10 results\",\"time\":{\"start\":1788985751629,\"end\":1788985751718}},\"id\":\"prt_g001a087dc32ea00146tzI6hWQ\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788985751746,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc38a8001GmcESKhW0\",\"reason\":\"tool-calls\",\"snapshot\":\"3bcfd7880a5b576689b0a8e5dd3e654522319798\",\"messageID\":\"msg_g001a087dbf6c6001i6M8UQKAI\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"step-finish\",\"tokens\":{\"total\":32144,\"input\":14319,\"output\":225,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":17600}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788985760823,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc5c35001L9P5AtOvy\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"snapshot\":\"20d5050daf9ac7d207b5d70574b5e91def251278\",\"type\":\"step-start\"}}\n{\"type\":\"text\",\"timestamp\":1788985761486,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc5cc90020MCy9u15D\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"text\",\"text\":\"The harness repo at `/mnt/workdrive/issue161-harness` is where newer rounds live. Let me check for issue-311 there.\",\"time\":{\"start\":1788985760969,\"end\":1788985761484}}}\n{\"type\":\"tool_use\",\"timestamp\":1788985761510,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_70928ccc59944f7bb4e2499b\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"ls /mnt/workdrive/issue161-harness/research/ 2>/dev/null; ls /mnt/workdrive/issue161-harness/research/issue-311/ 2>/dev/null\",\"description\":\"Check harness repo research dirs for issue-311\"},\"output\":\"batch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-163\\nissue-245-research.md\\nissue-256\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nissue-310\\nissue-311\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\nstage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n\",\"metadata\":{\"output\":\"batch-processing\\nbuild-mode\\ncontract-testing\\nfull-audit\\ninterstitial-tools\\nissue-161\\nissue-163\\nissue-245-research.md\\nissue-256\\nissue-260\\nissue-261\\nissue-262\\nissue-263\\nissue-310\\nissue-311\\nllm-craft-api\\nllm-craft-deep\\nquality-tools\\nresearch-methodology\\nrune-efficiency\\nsafety-override\\ntextual-craft\\ntool-calling\\nuncensored-models\\nv1\\nworkflow-audit\\nstage_OUTWARD.events.jsonl\\nstage_OUTWARD.mimo.err.log\\ntask_tree.json\\ntelemetry\\n\",\"exit\":0,\"description\":\"Check harness repo research dirs for issue-311\",\"truncated\":false},\"title\":\"Check harness repo research dirs for issue-311\",\"time\":{\"start\":1788985761507,\"end\":1788985761509}},\"id\":\"prt_g001a087dc5cce001YC3DGSyYe\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788985761568,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc5ee600128yO42VJd\",\"reason\":\"tool-calls\",\"snapshot\":\"e2b2842c991f69610cfd48e190e5a5a0de813d12\",\"messageID\":\"msg_g001a087dc38e50014nr7JHrJm\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"type\":\"step-finish\",\"tokens\":{\"total\":36707,\"input\":4631,\"output\":140,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":31936}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788985771881,\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"part\":{\"id\":\"prt_g001a087dc8765001dRJ3AnAxr\",\"messageID\":\"msg_g001a087dc5f7a001V4Yw8KDjz\",\"sessionID\":\"ses_-ffe5f782475a4ffegE8Ix6aZJ\",\"snapshot\":\"ce241fa16e7c8e51bd854585dec5e249e795a332\",\"type\":\"step-start\"}}\n---\n---\n","exit":0,"description":"Read issue-311 task tree and stage events","truncated":true,"outputPath":"/home/nick/.local/share/mimocode/tool-output/tool_g001a087dc8e4b001uU5xUK5iD"},"title":"Read issue-311 task tree and stage events","time":{"start":1788985773635,"end":1788985773645}},"id":"prt_g001a087dc8767001j6eaZ402o","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dc5f7a001V4Yw8KDjz"}} +{"type":"step_finish","timestamp":1788985773672,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dc8e4f001jZSq7uMxK","reason":"tool-calls","snapshot":"d406ac95c098232e6f3539192c51c7298599f3db","messageID":"msg_g001a087dc5f7a001V4Yw8KDjz","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":36949,"input":196,"output":145,"reasoning":0,"cache":{"write":0,"read":36608}},"cost":0}} +{"type":"step_start","timestamp":1788985793495,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dcdbd5001iB7n9RVBj","messageID":"msg_g001a087dc8e8c001TFwCogQHP","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"2ff811953117a8b2c7de828b7fc310b42367f4a6","type":"step-start"}} +{"type":"text","timestamp":1788985794292,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dcde95001gvNrD0SdU","messageID":"msg_g001a087dc8e8c001TFwCogQHP","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"This session IS the dispatched OUTWARD stage for issue-311 (the events log matches this session ID). ISSUE + INWARD are done (deterministic). Let me check the stage contract/prompt and the issue-310 artifact format to match conventions.","time":{"start":1788985794197,"end":1788985794289}}} +{"type":"tool_use","timestamp":1788985794310,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_a5f2fbd5f5614afdb8d1d1eb","state":{"status":"completed","input":{"command":"cat /mnt/workdrive/research-stage/prompts/research-stages/outward.md 2>/dev/null; echo ===; ls /mnt/workdrive/issue161-harness/research/issue-310/ 2>/dev/null","description":"Read OUTWARD stage prompt and issue-310 artifacts"},"output":"# Research Stage: OUTWARD\n\n## Process capsule\n- Issue: {{ISSUE_NUMBER}}\n- Round: {{ROUND}}\n- Stage: OUTWARD (of 7)\n- Prior artifacts: {{PRIOR_ARTIFACTS}}\n- Blocking unknowns: {{BLOCKING_UNKNOWNS}}\n\n## Your task\nFind and read enough reliable external evidence to answer the external research questions.\n\n## You MUST\n\nDISCOVERY:\n- Issue real searches (webfetch of search engines, documentation sites)\n- Preserve query/tool receipts\n- Identify candidate sources with stable IDs (O1, O2, ...)\n- Classify each source (official docs, GitHub, academic, blog)\n- Select a bounded relevant set for reading\n\nREADING:\n- Actually fetch/read the selected sources\n- Extract evidence claims relevant to the subquestions\n- Record limitations and contradictions\n- Preserve source IDs and fetch/read receipts\n\n## You MUST NOT\n- Inspect unrelated Rune code\n- Design the Rune solution\n- Perform INNOVATION (that's the next stage)\n- Perform SYNTHESIS\n- Modify production code\n- Continue searching after the evidence requirement is satisfied\n- Treat search snippets as evidence (actually read the pages)\n\n## STOP CONDITION\nStop when each required external subquestion has:\n- Adequate evidence, OR\n- Explicit contradictory evidence, OR\n- A named unresolved gap\n\n## Output\nProduce your stage artifact as structured text with:\n- DISCOVERY: queries issued, sources discovered with URLs\n- READING: sources actually read, evidence extracted\n- LIMITATIONS: what the sources don't cover\n- CONTRADICTIONS: where sources disagree\n\nEnd with:\n```\nStageCompleted: OUTWARD\n```\nzsh:1: == not found\n","metadata":{"output":"# Research Stage: OUTWARD\n\n## Process capsule\n- Issue: {{ISSUE_NUMBER}}\n- Round: {{ROUND}}\n- Stage: OUTWARD (of 7)\n- Prior artifacts: {{PRIOR_ARTIFACTS}}\n- Blocking unknowns: {{BLOCKING_UNKNOWNS}}\n\n## Your task\nFind and read enough reliable external evidence to answer the external research questions.\n\n## You MUST\n\nDISCOVERY:\n- Issue real searches (webfetch of search engines, documentation sites)\n- Preserve query/tool receipts\n- Identify candidate sources with stable IDs (O1, O2, ...)\n- Classify each source (official docs, GitHub, academic, blog)\n- Select a bounded relevant set for reading\n\nREADING:\n- Actually fetch/read the selected sources\n- Extract evidence claims relevant to the subquestions\n- Record limitations and contradictions\n- Preserve source IDs and fetch/read receipts\n\n## You MUST NOT\n- Inspect unrelated Rune code\n- Design the Rune solution\n- Perform INNOVATION (that's the next stage)\n- Perform SYNTHESIS\n- Modify production code\n- Continue searching after the evidence requirement is satisfied\n- Treat search snippets as evidence (actually read the pages)\n\n## STOP CONDITION\nStop when each required external subquestion has:\n- Adequate evidence, OR\n- Explicit contradictory evidence, OR\n- A named unresolved gap\n\n## Output\nProduce your stage artifact as structured text with:\n- DISCOVERY: queries issued, sources discovered with URLs\n- READING: sources actually read, evidence extracted\n- LIMITATIONS: what the sources don't cover\n- CONTRADICTIONS: where sources disagree\n\nEnd with:\n```\nStageCompleted: OUTWARD\n```\nzsh:1: == not found\n","exit":1,"description":"Read OUTWARD stage prompt and issue-310 artifacts","truncated":false},"title":"Read OUTWARD stage prompt and issue-310 artifacts","time":{"start":1788985794306,"end":1788985794308}},"id":"prt_g001a087dcde9f001aRBWaFqkJ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dc8e8c001TFwCogQHP"}} +{"type":"step_finish","timestamp":1788985794332,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dcdf050012V5dNWcgn","reason":"tool-calls","snapshot":"0ec18a33efdcb61df2834bd739da4fc0a22750c8","messageID":"msg_g001a087dc8e8c001TFwCogQHP","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":52757,"input":15354,"output":603,"reasoning":0,"cache":{"write":0,"read":36800}},"cost":0}} +{"type":"step_start","timestamp":1788985803720,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd03c5001adU17M27Z","messageID":"msg_g001a087dcdf5f001DsKnOeFjU","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"11397510c635cc96786c4586ddb8ce8d418faaa9","type":"step-start"}} +{"type":"tool_use","timestamp":1788985804479,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_03dcf740265a4b9bab7cd21b","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/issue161-harness/research/issue-310/; echo SEP; ls /mnt/workdrive/issue161-harness/research/issue-311/telemetry/","description":"List issue-310 artifacts and 311 telemetry"},"output":"outward.json\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\nSEP\nattempts.jsonl\n","metadata":{"output":"outward.json\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\nSEP\nattempts.jsonl\n","exit":0,"description":"List issue-310 artifacts and 311 telemetry","truncated":false},"title":"List issue-310 artifacts and 311 telemetry","time":{"start":1788985804474,"end":1788985804477}},"id":"prt_g001a087dd03c7001A0DFSZzZE","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dcdf5f001DsKnOeFjU"}} +{"type":"step_finish","timestamp":1788985804500,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd06be001j4shBJUBO","reason":"tool-calls","snapshot":"9b4a7f568632cd6cf0290ebe8f19b20273be12cc","messageID":"msg_g001a087dcdf5f001DsKnOeFjU","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":52703,"input":476,"output":67,"reasoning":0,"cache":{"write":0,"read":52160}},"cost":0}} +{"type":"step_start","timestamp":1788985810118,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd1cc4001jtmRVrQcd","messageID":"msg_g001a087dd06f9001PvUWcQB3E","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"f07bd520fd78a5b5b9997d2d8ab45cf26f0d2d86","type":"step-start"}} +{"type":"tool_use","timestamp":1788985811650,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_662bee43c1494f4f8824d7d9","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-310/outward.json'))\nprint(type(d))\nprint(list(d.keys()) if isinstance(d,dict) else len(d))\n\" 2>&1; grep -n \"StageCompleted\" /mnt/workdrive/issue161-harness/research/issue-310/outward.json 2>/dev/null | head -3","description":"Inspect issue-310 outward.json structure"},"output":"\n['stage', 'issue', 'round', 'subquestion', 'required_source_classes', 'queries', 'sources', 'evidence', 'limitations', 'contradictions']\n","metadata":{"output":"\n['stage', 'issue', 'round', 'subquestion', 'required_source_classes', 'queries', 'sources', 'evidence', 'limitations', 'contradictions']\n","exit":0,"description":"Inspect issue-310 outward.json structure","truncated":false},"title":"Inspect issue-310 outward.json structure","time":{"start":1788985811647,"end":1788985811649}},"id":"prt_g001a087dd1cc5001wmVcVSwYq","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dd06f9001PvUWcQB3E"}} +{"type":"step_finish","timestamp":1788985811675,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd22c2001IAdN1CQuD","reason":"tool-calls","snapshot":"64ea68b816de5cfba3c4e715b1c91cd5bb67a222","messageID":"msg_g001a087dd06f9001PvUWcQB3E","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":52999,"input":35287,"output":112,"reasoning":0,"cache":{"write":0,"read":17600}},"cost":0}} +{"type":"step_start","timestamp":1788985818068,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd3bd2001Y80yuB03i","messageID":"msg_g001a087dd22f700175fowEZrB","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"4456d51f1a02a794b4d19369670ecbfabcece12c","type":"step-start"}} +{"type":"tool_use","timestamp":1788985821928,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_f385f1deb2144f119b74049a","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-310/outward.json'))\nprint('stage:',d['stage'],'issue:',d['issue'],'round:',d['round'])\nprint('subquestion:',d['subquestion'])\nprint('required_source_classes:',d['required_source_classes'])\nprint('queries:',json.dumps(d['queries'])[:800])\nprint('n_sources:',len(d['sources']),'n_evidence:',len(d['evidence']))\nprint('sample source:',json.dumps(d['sources'][0],indent=1)[:600])\nprint('sample evidence:',json.dumps(d['evidence'][0],indent=1)[:600])\nprint('limitations:',json.dumps(d['limitations'])[:400])\nprint('contradictions:',json.dumps(d['contradictions'])[:400])\n\"","description":"Dump issue-310 outward.json field samples"},"output":"stage: OUTWARD issue: 310 round: 1\nsubquestion: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path — model_time/tool_time always 0\nrequired_source_classes: ['repo']\nqueries: [{\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"model_time|tool_time\", \"scope\": \"whole repo\", \"hits\": 51, \"receipt\": \"matches in research/issue-310/task_tree.json, telemetry/attempts.jsonl (3 attempt records), outward.json (prior attempt), scripts/stage_telemetry.py:13,140,141,155,156, src/rune/rune_operator.py:1574, src/rune/inference_trace.py:69, docs/inference-trace.md:35, plans/2026-09-05-p0-07-inference-trace.md:78,120,158, canary_p0_07.py, and 5 test files (test_p0_07_inference_trace.py, test_p1_01/02/03/06)\"}, {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start\", \"scope\": \"whole repo\", \"hits\": 16, \"receipt\": \"ALL matches confined to: scripts/stage_telemetry.py (docstring lines 24-25 + defin\nn_sources: 6 n_evidence: 7\nsample source: {\n \"id\": \"O1\",\n \"path\": \"scripts/stage_telemetry.py\",\n \"classification\": \"PRIMARY\",\n \"locus\": \"lines 2-28 (module docstring, OBS-1 epic #284/#287), 45-79 (StageAttempt.__init__: window lists + counters), 83-115 (mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end), 119-128 (finish() force-closes unclosed windows), 132-143 (summary(): model_time_s/tool_time_s = summed window pairs, explicitly non-additive), 186-218 (TelemetryLedger append-only persist, never raises)\"\n}\nsample evidence: {\n \"id\": \"E1\",\n \"source\": \"O1\",\n \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence) plus counters (model_calls, tool_calls, sources_discovered, ...); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish(); categories are explicitly non-additive vs wall_time.\",\n \"locus\": \"scripts/stage_telemetry.py:59-79, 83-128, 132-143, 154-163\"\n}\nlimitations: [\"Grep receipts cover all file types repo-wide for the mark_* pattern (16 hits, no include filter), so the 'test-only invocation' finding is not limited to *.py.\", \"Whether ResearchEventRelay itself or the mimo subprocess could emit window events that the executor drops was not traced into research_event_relay.py internals; however, grep for StageAttempt/mark_* shows the relay module never referen\ncontradictions: [\"None found on B1 itself: static evidence (zero production call sites for mark_* markers, O2/O3) and empirical evidence (attempts.jsonl all zeros including a 315s attempt, O4) agree.\", \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) \\u2014 same 'model_time' naming family, different pipelines; any source\n","metadata":{"output":"stage: OUTWARD issue: 310 round: 1\nsubquestion: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path — model_time/tool_time always 0\nrequired_source_classes: ['repo']\nqueries: [{\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"model_time|tool_time\", \"scope\": \"whole repo\", \"hits\": 51, \"receipt\": \"matches in research/issue-310/task_tree.json, telemetry/attempts.jsonl (3 attempt records), outward.json (prior attempt), scripts/stage_telemetry.py:13,140,141,155,156, src/rune/rune_operator.py:1574, src/rune/inference_trace.py:69, docs/inference-trace.md:35, plans/2026-09-05-p0-07-inference-trace.md:78,120,158, canary_p0_07.py, and 5 test files (test_p0_07_inference_trace.py, test_p1_01/02/03/06)\"}, {\"kind\": \"QUERY\", \"tool\": \"Grep\", \"pattern\": \"mark_model_start|mark_model_end|mark_tool_start|mark_tool_end|mark_gate_start|mark_persistence_start\", \"scope\": \"whole repo\", \"hits\": 16, \"receipt\": \"ALL matches confined to: scripts/stage_telemetry.py (docstring lines 24-25 + defin\nn_sources: 6 n_evidence: 7\nsample source: {\n \"id\": \"O1\",\n \"path\": \"scripts/stage_telemetry.py\",\n \"classification\": \"PRIMARY\",\n \"locus\": \"lines 2-28 (module docstring, OBS-1 epic #284/#287), 45-79 (StageAttempt.__init__: window lists + counters), 83-115 (mark_model_start/end, mark_tool_start/end, mark_gate_start/end, mark_persistence_start/end), 119-128 (finish() force-closes unclosed windows), 132-143 (summary(): model_time_s/tool_time_s = summed window pairs, explicitly non-additive), 186-218 (TelemetryLedger append-only persist, never raises)\"\n}\nsample evidence: {\n \"id\": \"E1\",\n \"source\": \"O1\",\n \"claim\": \"StageAttempt defines four window-marker pairs (model, tool, gate, persistence) plus counters (model_calls, tool_calls, sources_discovered, ...); summary() derives model_time_s and tool_time_s by summing (end-start) monotonic window pairs; unclosed windows are force-closed at finish(); categories are explicitly non-additive vs wall_time.\",\n \"locus\": \"scripts/stage_telemetry.py:59-79, 83-128, 132-143, 154-163\"\n}\nlimitations: [\"Grep receipts cover all file types repo-wide for the mark_* pattern (16 hits, no include filter), so the 'test-only invocation' finding is not limited to *.py.\", \"Whether ResearchEventRelay itself or the mimo subprocess could emit window events that the executor drops was not traced into research_event_relay.py internals; however, grep for StageAttempt/mark_* shows the relay module never referen\ncontradictions: [\"None found on B1 itself: static evidence (zero production call sites for mark_* markers, O2/O3) and empirical evidence (attempts.jsonl all zeros including a 315s attempt, O4) agree.\", \"Near-contradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) \\u2014 same 'model_time' naming family, different pipelines; any source\n","exit":0,"description":"Dump issue-310 outward.json field samples","truncated":false},"title":"Dump issue-310 outward.json field samples","time":{"start":1788985821923,"end":1788985821927}},"id":"prt_g001a087dd3bd3001mJL3upOs4","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dd22f700175fowEZrB"}} +{"type":"step_finish","timestamp":1788985821950,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd4ae8001qFdlaNsoZ","reason":"tool-calls","snapshot":"1a39ebe2709de355a06aa0962a93ba9472d5e59d","messageID":"msg_g001a087dd22f700175fowEZrB","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":53243,"input":115,"output":200,"reasoning":0,"cache":{"write":0,"read":52928}},"cost":0}} +{"type":"step_start","timestamp":1788985832636,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd74ba001nwnnQwKZ7","messageID":"msg_g001a087dd4b1e001JNOTvJzs0","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"20161eca65cf4e4da92538b765c918086d248c70","type":"step-start"}} +{"type":"text","timestamp":1788985833330,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd74bb0014y1g0RKcg","messageID":"msg_g001a087dd4b1e001JNOTvJzs0","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"I have the artifact schema from issue-310. Now I understand the task: the stage prompt says external evidence with real searches. Let me also read the Gitea issue #311/#158 for full context (REQUIRED SOURCE CLASSES: repo, external — INWARD already did repo-side).","time":{"start":1788985832635,"end":1788985833329}}} +{"type":"tool_use","timestamp":1788985833349,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_399d08ab61cd4cc1b49575bc","state":{"status":"completed","input":{"command":"cd /mnt/workdrive/rune && git remote -v 2>/dev/null | head -2; which gitea tea 2>/dev/null; env | grep -i gitea | head -5","description":"Check Gitea access for issue text"},"output":"gitea\thttps://conductor:6189e908e2eebcf698ee28cda11dec5fb77c34cf@gitea.thingscouldgetdicey.online/conductor/rune.git (fetch)\ngitea\thttps://conductor:6189e908e2eebcf698ee28cda11dec5fb77c34cf@gitea.thingscouldgetdicey.online/conductor/rune.git (push)\ngitea not found\ntea not found\nGITEA_TOKEN=6189e908e2eebcf698ee28cda11dec5fb77c34cf\n","metadata":{"output":"gitea\thttps://conductor:6189e908e2eebcf698ee28cda11dec5fb77c34cf@gitea.thingscouldgetdicey.online/conductor/rune.git (fetch)\ngitea\thttps://conductor:6189e908e2eebcf698ee28cda11dec5fb77c34cf@gitea.thingscouldgetdicey.online/conductor/rune.git (push)\ngitea not found\ntea not found\nGITEA_TOKEN=6189e908e2eebcf698ee28cda11dec5fb77c34cf\n","exit":0,"description":"Check Gitea access for issue text","truncated":false},"title":"Check Gitea access for issue text","time":{"start":1788985833346,"end":1788985833348}},"id":"prt_g001a087dd7516001By79IIWEA","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dd4b1e001JNOTvJzs0"}} +{"type":"step_finish","timestamp":1788985833372,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd7784002XF5wWQooy","reason":"tool-calls","snapshot":"eac090d18dd08c0b87fc879d259a6b0ecb66dde7","messageID":"msg_g001a087dd4b1e001JNOTvJzs0","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":54173,"input":989,"output":128,"reasoning":0,"cache":{"write":0,"read":53056}},"cost":0}} +{"type":"step_start","timestamp":1788985840913,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd950f001R8vIuegAM","messageID":"msg_g001a087dd77ba001lacecGbHZ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"d3e3f36739f057996a58297cd8d9ab4e07621f3f","type":"step-start"}} +{"type":"tool_use","timestamp":1788985843188,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_3438cf5ad98745ea92eb0bb0","state":{"status":"completed","input":{"command":"curl -s \"https://gitea.thingscouldgetdicey.online/api/v1/repos/conductor/rune/issues/311\" -H \"Authorization: token $GITEA_TOKEN\" | python3 -c \"import json,sys; d=json.load(sys.stdin); print(d.get('title')); print('---'); print((d.get('body') or '')[:3000])\"","description":"Fetch Gitea issue 311 body"},"output":"ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)\n---\n**Pre-registered ADAPT-1 baseline task B2** (implements open issue #158). Baseline master: 243474c6. Expected future route: STANDARD_CHANGE.\n\n**Why this class:** desired change clear (generated wiring-status/architecture-truth report); existing Rune architecture owns the surfaces (orchestration reporting pattern exists — benchmark.py, PRD state generators); no consequential technology selection; local repo evidence sufficient; benefits from a small design contract (report schema, data sources); not expensive to reverse.\n\n**Acceptance criteria (from #158, bounded for baseline):** a generated report that maps which modules are wired where — import graph summary + authority-map coverage — committed as a script + test, regenerable deterministically.\n\n**Known dependencies:** config/v1_authority_map.json (exists), module scan (walkman pattern exists).\n\n**Prior research/artifacts:** none required — established pattern.\n","metadata":{"output":"ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)\n---\n**Pre-registered ADAPT-1 baseline task B2** (implements open issue #158). Baseline master: 243474c6. Expected future route: STANDARD_CHANGE.\n\n**Why this class:** desired change clear (generated wiring-status/architecture-truth report); existing Rune architecture owns the surfaces (orchestration reporting pattern exists — benchmark.py, PRD state generators); no consequential technology selection; local repo evidence sufficient; benefits from a small design contract (report schema, data sources); not expensive to reverse.\n\n**Acceptance criteria (from #158, bounded for baseline):** a generated report that maps which modules are wired where — import graph summary + authority-map coverage — committed as a script + test, regenerable deterministically.\n\n**Known dependencies:** config/v1_authority_map.json (exists), module scan (walkman pattern exists).\n\n**Prior research/artifacts:** none required — established pattern.\n","exit":0,"description":"Fetch Gitea issue 311 body","truncated":false},"title":"Fetch Gitea issue 311 body","time":{"start":1788985843181,"end":1788985843187}},"id":"prt_g001a087dd9510002a1Vqy03Cx","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dd77ba001lacecGbHZ"}} +{"type":"step_finish","timestamp":1788985843211,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dd9df4001zPqBhOGYZ","reason":"tool-calls","snapshot":"7f865b511f89725e6f747305de13d34f320b7518","messageID":"msg_g001a087dd77ba001lacecGbHZ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":54424,"input":238,"output":106,"reasoning":0,"cache":{"write":0,"read":54080}},"cost":0}} +{"type":"step_start","timestamp":1788985847341,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087ddae2c0011IzE8SKfE","messageID":"msg_g001a087dd9e2a001BGULzzd5k","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"a3b1e88358baab43e60748ac1404f9d10e8b9849","type":"step-start"}} +{"type":"tool_use","timestamp":1788985849601,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_77de754d32a9414bbcc649e7","state":{"status":"completed","input":{"command":"curl -s \"https://gitea.thingscouldgetdicey.online/api/v1/repos/conductor/rune/issues/158\" -H \"Authorization: token $GITEA_TOKEN\" | python3 -c \"import json,sys; d=json.load(sys.stdin); print(d.get('title')); print('---'); print((d.get('body') or '')[:4000])\"","description":"Fetch Gitea issue 158 body"},"output":"Generated wiring-status / architecture-truth report\n---\n## Plain summary (EXTERNAL_PROJECT_LESSONS_HANDOFF §4, Mnemosyne 'partially wired' honesty)\n\nMake architectural claims machine-checkable. Generate (from code/registries — NOT manually maintained) a report:\n\n| Subsystem | Defined | Live caller | Causal path | Tests | Canary | Benchmark |\n|---|---|---|---|---|---|---|\n\n## Initial targets\nContextBroker; CapabilityBroker; AttentionCapsule; OutcomeLedger; warm-neuron learning; abstraction; self-model; skills; CognitiveLoop; DreamCycle; AutonomyLoop; model routing; permissions; event/episodic stores.\n\n## Acceptance\n- [ ] The report identifies at least one deliberately disconnected test subsystem as not-live/not-causal (demonstrated)\n- [ ] Generated from import graphs/registries — no hand-maintained truth\n- [ ] Emitted in CI or on demand (`/wiring` style)\n","metadata":{"output":"Generated wiring-status / architecture-truth report\n---\n## Plain summary (EXTERNAL_PROJECT_LESSONS_HANDOFF §4, Mnemosyne 'partially wired' honesty)\n\nMake architectural claims machine-checkable. Generate (from code/registries — NOT manually maintained) a report:\n\n| Subsystem | Defined | Live caller | Causal path | Tests | Canary | Benchmark |\n|---|---|---|---|---|---|---|\n\n## Initial targets\nContextBroker; CapabilityBroker; AttentionCapsule; OutcomeLedger; warm-neuron learning; abstraction; self-model; skills; CognitiveLoop; DreamCycle; AutonomyLoop; model routing; permissions; event/episodic stores.\n\n## Acceptance\n- [ ] The report identifies at least one deliberately disconnected test subsystem as not-live/not-causal (demonstrated)\n- [ ] Generated from import graphs/registries — no hand-maintained truth\n- [ ] Emitted in CI or on demand (`/wiring` style)\n","exit":0,"description":"Fetch Gitea issue 158 body","truncated":false},"title":"Fetch Gitea issue 158 body","time":{"start":1788985849594,"end":1788985849600}},"id":"prt_g001a087ddae2d0011oX0cUI2m","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dd9e2a001BGULzzd5k"}} +{"type":"step_finish","timestamp":1788985849630,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087ddb701001btCGC1Sqy","reason":"tool-calls","snapshot":"dea01849819608eb6edbf1bb9aeda9584dca57f6","messageID":"msg_g001a087dd9e2a001BGULzzd5k","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":54743,"input":303,"output":104,"reasoning":0,"cache":{"write":0,"read":54336}},"cost":0}} +{"type":"step_start","timestamp":1788985873169,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de130f0011w0GeWvFV","messageID":"msg_g001a087ddb748001R4RDNAYeO","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"b613c2f29cef0880d1d98d68992165ae774f1ebe","type":"step-start"}} +{"type":"text","timestamp":1788985877955,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de15a4001j7cDuciys","messageID":"msg_g001a087ddb748001R4RDNAYeO","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Now I have the full picture. The research question: external evidence on how established tooling/practice does generated architecture-truth/wiring-status reporting. Let me check the issue-310 artifact tail convention, then run discovery searches.","time":{"start":1788985873828,"end":1788985877953}}} +{"type":"tool_use","timestamp":1788985877964,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_18a022fd4cfa49e79c8e68fd","state":{"status":"completed","input":{"command":"tail -c 300 /mnt/workdrive/issue161-harness/research/issue-310/outward.json","description":"Check artifact tail convention"},"output":"ontradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) — same 'model_time' naming family, different pipelines; any source claiming 'model_time is populated' would be true of the former and false of the latter.\"\n ]\n}\n","metadata":{"output":"ontradiction resolved by disambiguation: InferenceTrace.model_time_ms (populated, O5) vs StageAttempt.model_time_s (always 0, O1/O2/O4) — same 'model_time' naming family, different pipelines; any source claiming 'model_time is populated' would be true of the former and false of the latter.\"\n ]\n}\n","exit":0,"description":"Check artifact tail convention","truncated":false},"title":"Check artifact tail convention","time":{"start":1788985877960,"end":1788985877962}},"id":"prt_g001a087de15ac001bEK1a4RS5","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087ddb748001R4RDNAYeO"}} +{"type":"step_finish","timestamp":1788985877986,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de25cb001kokjMnaMW","reason":"tool-calls","snapshot":"d82eae9338f2b8c95e143ca5fb92ccbe28e54fbe","messageID":"msg_g001a087ddb748001R4RDNAYeO","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":55496,"input":296,"output":544,"reasoning":0,"cache":{"write":0,"read":54656}},"cost":0}} +{"type":"step_start","timestamp":1788985888854,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de5055001wOC4Q3niy","messageID":"msg_g001a087de26040015Z20wZZsF","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"8775883f767fcb646a329ce7d1d935975c825ef1","type":"step-start"}} +{"type":"tool_use","timestamp":1788985889440,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_93f460362a1247ce9260e90e","state":{"status":"completed","input":{"command":"date -u +%Y-%m-%dT%H:%M:%SZ","description":"Timestamp for receipts"},"output":"2026-09-09T20:31:29Z\n","metadata":{"output":"2026-09-09T20:31:29Z\n","exit":0,"description":"Timestamp for receipts","truncated":false},"title":"Timestamp for receipts","time":{"start":1788985889436,"end":1788985889439}},"id":"prt_g001a087de50560015hcsDWFz0","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de26040015Z20wZZsF"}} +{"type":"step_finish","timestamp":1788985889465,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de52a00019eat3T0mi","reason":"tool-calls","snapshot":"69f95fd55c18eba191e5285edccd59d02c0557f7","messageID":"msg_g001a087de26040015Z20wZZsF","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":55153,"input":145,"output":32,"reasoning":0,"cache":{"write":0,"read":54976}},"cost":0}} +{"type":"step_start","timestamp":1788985895244,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de694b0014ZyiG0BmQ","messageID":"msg_g001a087de52db001JCJrR2ILw","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"8c029c7df05f9308b5b09562803dfc5e85aff1a1","type":"step-start"}} +{"type":"text","timestamp":1788985898088,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de694c001PGr9AxU7B","messageID":"msg_g001a087de52db001JCJrR2ILw","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Now running discovery — 4 parallel web searches on architecture-truth/wiring-report tooling:","time":{"start":1788985895244,"end":1788985898086}}} +{"type":"tool_use","timestamp":1788985898957,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_df1842015261458c89c807f7","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=architecture+report+generated+from+import+graph+%22no+hand-maintained%22+dependency+diagram+CI"},"output":"architecture report generated from import graph "no hand-maintained" dependency diagram CI at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n No results found for architecture report generated from import graph "no hand-maintained" dependency diagram CI\n \n Suggestions:Check spellingTry related keywordsRemove unnecessary punctuation\n \n \n \n\n\n \n \n\n \n\n \n \n \n \n \n\n \n \n \n \n \n\n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=architecture+report+generated+from+import+graph+%22no+hand-maintained%22+dependency+diagram+CI (text/html; charset=UTF-8)","time":{"start":1788985898087,"end":1788985898956}},"id":"prt_g001a087de69b00018b8ASHWZ1","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de52db001JCJrR2ILw"}} +{"type":"tool_use","timestamp":1788985898975,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_f560ca4211e34ca2b2203ca8","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=import-linter+enforce+architecture+layers+python+contract"},"output":"import-linter enforce architecture layers python contract at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n Layers - Import Linter\n \n\n \n\n \n \n \n \n \n \n \n \n \n import-linter.readthedocs.io/en/stable/contract_types/layers/\n \n \n \n \n \n\n \n \n Home Contract types Layers Layers Type name: layers Layers contracts enforce a 'layered architecture', where higher layers may depend on lower layers, but not the other way around. Configuration options layers: An ordered list with the name of each layer module. If containers are specified too, then these names must be relative to the container. The order is from higher to lower level layers ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n import-linter/docs/contract_types/layers.md at main - GitHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/seddonym/import-linter/blob/main/docs/contract_types/layers.md\n \n \n \n \n \n\n \n \n Lint your Python architecture. Contribute to seddonym/import-linter development by creating an account on GitHub.\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - seddonym/import-linter: Lint your Python architecture.\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/seddonym/import-linter\n \n \n \n \n \n\n \n \n Import Linter allows you to impose constraints on the imports between your Python modules. It also provides a browser-based user interface for exploring the architecture of any Python package. Get started.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Import Linter\n \n\n \n\n \n \n \n \n \n \n \n \n \n import-linter.readthedocs.io\n \n \n \n \n \n\n \n \n Import Linter allows you to impose constraints on the imports between your Python modules. It also provides a browser-based user interface for exploring the architecture of any Python package.\n \n \n\n \n \n \n \n \n \n \n \n \n \n import-linter · PyPI\n \n\n \n\n \n \n \n \n \n \n \n \n \n pypi.org/project/import-linter/\n \n \n     2026-09-04T00:00:00.0000000\n \n \n \n \n\n \n \n Import Linter allows you to impose constraints on the imports between your Python modules. It also provides a browser-based user interface for exploring the architecture of any Python package.\n \n \n\n \n \n \n \n \n \n \n \n \n \n seddonym/import-linter | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/seddonym/import-linter\n \n \n     2025-12-25T00:00:00.0000000\n \n \n \n \n\n \n \n Import Linter is a command-line tool that enforces architectural rules on Python imports. It analyzes the import relationships in a Python codebase and checks them against user-defined contracts that specify which modules can import from which other modules.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Layers Contract | seddonym/import-linter | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/seddonym/import-linter/4.1-layers-contract\n \n \n     2025-12-25T00:00:00.0000000\n \n \n \n \n\n \n \n This page documents the LayersContract, which enforces unidirectional layered architecture patterns. Higher layers may depend on lower layers, but not the other way around. The contract supports repeating layer patterns across multiple containers and provides flexible configuration for optional layers and exhaustive validation.\n \n \n\n \n \n \n \n \n \n \n \n \n \n ADR-0001: Enforce boundaries with import-linter - FastAPI Hexagonal ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n matthiaseg.github.io/python-hexagonal-architecture-template/adr/0001-enforce-hexagonal-boundaries-with-import-linter/\n \n \n     2026-07-01T00:00:00.0000000\n \n \n \n \n\n \n \n We enforce the layer boundaries in CI with import-linter. Three contracts: A layerscontract ordering app.infrastructure→ app.application→ app.domain, so no inner layer may import an outer one. A forbiddencontract barring app.applicationfrom importing infrastructure libraries (sqlalchemy, fastapi, loguru, alembic, starlette, …).\n \n \n\n \n \n \n \n \n \n \n \n \n \n Enforce architectural import boundaries in Python codebases with Import ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n agentskillexchange.com/skills/enforce-architectural-import-boundaries-in-python-codebases-with-import-linter/\n \n \n \n \n \n\n \n \n Use Import Linter when an agent needs to guard a Python architecture against gradual boundary erosion. The agent defines contracts such as layered dependencies, forbidden imports, or independence rules, runs the checker, and turns violations into review comments or cleanup tasks.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Linter for Python Architecture - Roman Imankulov\n \n\n \n\n \n \n \n \n \n \n \n \n \n roman.pt/posts/python-architecture-linter/\n \n \n \n \n \n\n \n \n I was playing with import-linter, and it looks promising. What import-linter does In Python projects, we define dependencies by imports. If module A imports module B, then A depends on B. import-linter lets you specify the rules that declaratively constrain that dependency flow. In a config file, you define so-called contracts.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=import-linter+enforce+architecture+layers+python+contract (text/html; charset=UTF-8)","time":{"start":1788985898088,"end":1788985898973}},"id":"prt_g001a087de6c93001Ro719bjGR","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de52db001JCJrR2ILw"}} +{"type":"tool_use","timestamp":1788985899037,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_0d1dd3e8ed0c4d74a77f95ea","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=madge+dependency-cruiser+visualize+module+dependencies+report"},"output":"madge dependency-cruiser visualize module dependencies report at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n GitHub - pahen/madge: Create graphs from your CommonJS, AMD or ES6 ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/pahen/madge\n \n \n \n \n \n\n \n \n Madgeis a developer tool for generating a visual graph of your module dependencies, finding circular dependencies, and giving you other useful info. Joel Kemp's awesome dependency-treeis used for extracting the dependency tree.\n \n \n\n \n \n \n \n \n \n \n \n \n \n madge - npm\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.npmjs.com/package/madge\n \n \n \n \n \n\n \n \n Madge is a developer tool for generating a visual graph of your module dependencies, finding circular dependencies, and giving you other useful info. Joel Kemp's awesome dependency-tree is used for extracting the dependency tree.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Madge — Visualize Module Dependencies as Graphs · TokRepo\n \n\n \n\n \n \n \n \n \n \n \n \n \n tokrepo.com/en/workflows/asset-5578f72d\n \n \n     2026-07-14T00:00:00.0000000\n \n \n \n \n\n \n \n Madge generates visual dependency graphs from your JavaScript, TypeScript, or CSS modules, helping you find circular dependencies and understand project structure at a glance.\n \n \n\n \n \n \n \n \n \n \n \n \n \n dependency-cruiser - npm\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.npmjs.com/package/dependency-cruiser\n \n \n \n \n \n\n \n \n Validate and visualize dependencies. With your rules. JavaScript, TypeScript, CoffeeScript. ES6, CommonJS, AMD.. Latest version: 18.1.1, last published: 2 hours ago. Start using dependency-cruiser in your project by running `npm i dependency-cruiser`. There are 137 other projects in the npm registry using dependency-cruiser.\n \n \n\n \n \n \n \n \n \n \n \n \n \n How can Madge help you visualize JavaScript project dependencies ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n densitylabs.io/blog/how-can-madge-help-you-visualize-javascript-project-dependencies/\n \n \n \n \n \n\n \n \n Madge is a powerful developer tool that provides insights into the dependency structure of JavaScript and CSS preprocessors projects. It allows developers to generate visual graphs of module dependencies, identify circular dependencies, and offer other useful information that aids in maintaining the health and clarity of codebases.\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - msoftware/node-madge: Create graphs from your CommonJS or AMD ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/msoftware/node-madge\n \n \n \n \n \n\n \n \n MaDGe - Module Dependency Graph Create graphs from your CommonJS or AMD module dependencies. Could also be useful for finding circular dependencies in your code. Tested on Node.js and RequireJS projects. Dependencies are calculated using static code analysis.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Visualize dependencies for your codebase with Madge graphs\n \n\n \n\n \n \n \n \n \n \n \n \n \n omakoleg.gitlab.io/posts/madge/\n \n \n \n \n \n\n \n \n Visualize dependencies for your codebase with Madge graphs Fri, Nov 1, 2019 Lets see some use cases for madge, tool to generate nice visualizations for dependencies. Madge: Madge is a developer tool for generating a visual graph of your module dependencies, finding circular dependencies, and give you other useful info. Quote from official ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Madge: Detecting Circular Dependencies in TypeScript Projects\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.xjavascript.com/blog/madge-circular-dependency-typescript/\n \n \n     2026-07-19T00:00:00.0000000\n \n \n \n \n\n \n \n In large TypeScript projects, managing dependencies between modules is crucial for maintainability and scalability. Circular dependencies, where two or more modules depend on each other either directly or indirectly, can lead to hard-to-debug issues and make the codebase more brittle. Madge is a powerful tool that helps developers detect these circular dependencies in TypeScript projects. This ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Madge with TypeScript: A Comprehensive Guide - xjavascript.com\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.xjavascript.com/blog/madge-typescript/\n \n \n     2026-01-16T00:00:00.0000000\n \n \n \n \n\n \n \n In the world of software development, maintaining a well-structured and understandable codebase is crucial. As projects grow, the complexity of dependencies between modules can quickly spiral out of control. Madge is a popular tool that can help visualize these dependencies, and when combined with TypeScript, it becomes even more powerful. This blog post will guide you through the fundamental ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n madge - Create graphs from your CommonJS, AMD or ES6 module dependencies\n \n\n \n\n \n \n \n \n \n \n \n \n \n libs.tech/project/4384018/madge\n \n \n \n \n \n\n \n \n Madge is a developer tool for generating a visual graph of your module dependencies, finding circular dependencies, and giving you other useful info. Joel Kemp's awesome dependency-tree is used for extracting the dependency tree.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=madge+dependency-cruiser+visualize+module+dependencies+report (text/html; charset=UTF-8)","time":{"start":1788985898090,"end":1788985899036}},"id":"prt_g001a087de71c9001TYCAvQ84l","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de52db001JCJrR2ILw"}} +{"type":"tool_use","timestamp":1788985899386,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_8863dd86fec34cbfa021eafa","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=pytest+architectural+fitness+functions+dependency+rules+plugin"},"output":"pytest architectural fitness functions dependency rules plugin at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n Writing plugins — pytest documentation\n \n\n \n\n \n \n \n \n \n \n \n \n \n docs.pytest.org/en/7.1.x/how-to/writing_plugins.html\n \n \n \n \n \n\n \n \n A plugin contains one or multiple hook functions. Writing hooksexplains the basics and details of how you can write a hook function yourself. pytestimplements all aspects of configuration, collection, running and reporting by calling well specified hooksof the following plugins:\n \n \n\n \n \n \n \n \n \n \n \n \n \n Writing plugins - pytest documentation\n \n\n \n\n \n \n \n \n \n \n \n \n \n docs.pytest.org/en/stable/how-to/writing_plugins.html\n \n \n \n \n \n\n \n \n A plugin contains one or multiple hook functions. Writing hooks explains the basics and details of how you can write a hook function yourself. pytest implements all aspects of configuration, collection, running and reporting by calling well specified hooks of the following plugins: builtin plugins: loaded from pytest's internal _pytest directory.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Python Pytest Architecture: Fixtures, Mocking & Property Testing (2026)\n \n\n \n\n \n \n \n \n \n \n \n \n \n dev.to/kaushikcoderpy/python-pytest-architecture-fixtures-mocking-property-testing-2026-4k4e\n \n \n     2026-04-07T00:00:00.0000000\n \n \n \n \n\n \n \n 4. The Mocking Matrix: The Humble Object Pattern How do you test a function that charges a credit card via an external API? If you hit the real network, your tests are slow and flaky. You must isolate code under test from external dependencies using Mocking Frameworks. unittest.mock: The standard library tool. Allows tracking calls and replacing functions using patch. pytest-mock: A Pytest ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - LukasNiessen/ArchUnitPython: ArchUnitPython is an architecture ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/LukasNiessen/ArchUnitPython\n \n \n \n \n \n\n \n \n Enforce architecture rules in Python projects. Check for dependency directions, detect circular dependencies, enforce coding standards and much more. Integrates with pytest and any other testing framework. Very simple setup and pipeline integration. Zero runtime dependencies.\n \n \n\n \n \n \n \n \n \n \n \n \n \n jwbargsten/pytest-archon | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/jwbargsten/pytest-archon\n \n \n     2025-11-22T00:00:00.0000000\n \n \n \n \n\n \n \n What is pytest-archon? pytest-archon is a tool for enforcing architectural boundaries in Python projects through dependency testing. It allows developers to define rules about which modules can import from which other modules, and then automatically verifies these rules as part of the test suite.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Plugin Architecture and Hooks | pytest-dev/pytest | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/pytest-dev/pytest/4.1-plugin-architecture-and-hooks\n \n \n     2026-04-16T00:00:00.0000000\n \n \n \n \n\n \n \n Plugin Architecture and Hooks Relevant source files This document describes pytest's extensible plugin system built on the pluggy library. It detail how pytest uses hook specifications, plugin registration, and directory-scoped proxies to enable a modular architecture where core features and external extensions operate on the same footing.\n \n \n\n \n \n \n \n \n \n \n \n \n \n archunitpython · PyPI\n \n\n \n\n \n \n \n \n \n \n \n \n \n pypi.org/project/archunitpython/\n \n \n \n \n \n\n \n \n Release history ArchUnitPython - Architecture Testing Enforce architecture rules in Python projects. Check for dependency directions, detect circular dependencies, enforce coding standards and much more. Integrates with pytest and any other testing framework. Very simple setup and pipeline integration. Zero runtime dependencies.\n \n \n\n \n \n \n \n \n \n \n \n \n \n PyTestArch · PyPI\n \n\n \n\n \n \n \n \n \n \n \n \n \n pypi.org/project/PyTestArch/\n \n \n \n \n \n\n \n \n dependencies. This evaluable can be used for multiple architectural rule checks; if you are using pytest, you could use a fixture for this evaluable object. Define an architectural rule from pytestarch import Rule rule = ( Rule() .modules_that() .are_named("project.src.moduleB") .should_not() .be_imported_by_modules_that()\n \n \n\n \n \n \n \n \n \n \n \n \n \n Configuring pytest-dependency — pytest-dependency 0.6.1 documentation\n \n\n \n\n \n \n \n \n \n \n \n \n \n pytest-dependency.readthedocs.io/en/stable/configuration.html\n \n \n \n \n \n\n \n \n Configuring pytest-dependency This section explains configuration options for pytest-dependency, but also options for pytest itself or other plugins that are recommended for the use with pytest-dependency.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Enforcing Python Architecture Rules with PyTestArch - andi-john-dev.de\n \n\n \n\n \n \n \n \n \n \n \n \n \n andi-john-dev.de/blog/python-architecture-rules-with-pytestarch\n \n \n     2026-04-28T00:00:00.0000000\n \n \n \n \n\n \n \n PyTestArchis a small library that lets you express architectural rules — "this package must not import that one", "models only depend on pydantic and stdlib" — as regular pytest tests. Inspired by Java's ArchUnit, it scans your imports, builds a dependency graph, and fails the build when someone crosses a line you drew.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=pytest+architectural+fitness+functions+dependency+rules+plugin (text/html; charset=UTF-8)","time":{"start":1788985898089,"end":1788985899384}},"id":"prt_g001a087de6eeb001vHCFjiOmE","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de52db001JCJrR2ILw"}} +{"type":"step_finish","timestamp":1788985899404,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087de7979001B56atkeMC","reason":"tool-calls","snapshot":"4539b9fe0d03a02d649ff7b01966e134034c1e59","messageID":"msg_g001a087de52db001JCJrR2ILw","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":55364,"input":2,"output":194,"reasoning":0,"cache":{"write":0,"read":55168}},"cost":0}} +{"type":"step_start","timestamp":1788985913737,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087deb187001gGuXAFIAy","messageID":"msg_g001a087de79b4001pFAIFwi1V","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"6d3ae98f563b68016610cf59ad5a49b7afb08376","type":"step-start"}} +{"type":"text","timestamp":1788985914878,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087deb37b001VUjU8xj8v","messageID":"msg_g001a087de79b4001pFAIFwi1V","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Three of four queries returned solid candidates (query 1 returned no results — recorded as such). One more discovery round to cover the \"liveness / not-live subsystem detection\" angle, then I'll read the selected sources.","time":{"start":1788985914235,"end":1788985914875}}} +{"type":"tool_use","timestamp":1788985915621,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_f45e29f1104e4457891e6216","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=ArchUnit+architecture+test+freeze+violations+report"},"output":"ArchUnit architecture test freeze violations report at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n Freezing Architecture Rules | TNG/ArchUnit | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/TNG/ArchUnit/2.3.2-freezing-architecture-rules\n \n \n     2026-05-09T00:00:00.0000000\n \n \n \n \n\n \n \n This page explains the mechanism for "freezing" violations of architecture rules in ArchUnit. This feature is primarily designed to help teams introduce ArchUnit into legacy codebases incrementally by\n \n \n\n \n \n \n \n \n \n \n \n \n \n ArchUnit User Guide\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.archunit.org/userguide/html/000_Index.html\n \n \n \n \n \n\n \n \n ArchUnit is a free, simple and extensible library for checking the architecture of your Java code. That is, ArchUnit can check dependencies between packages and classes, layers and slices, check for cyclic dependencies and more. It does so by analyzing given Java bytecode, importing all classes into a Java code structure. ArchUnit's main focus is to automatically test architecture and coding ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n archunit - If violations are fixed, FreezingArchRule automatically ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n stackoverflow.com/questions/77046215/if-violations-are-fixed-freezingarchrule-automatically-reduce-the-stored-violat\n \n \n \n \n \n\n \n \n In ArchUnit Tests, If violations are fixed, FreezingArchRule will automatically reduce the known stored violations to prevent any regression.Is there any way to stop this behaviour?\n \n \n\n \n \n \n \n \n \n \n \n \n \n Violation Reporting | ts-archunit\n \n\n \n\n \n \n \n \n \n \n \n \n \n nielspeter.github.io/ts-archunit/violation-reporting.html\n \n \n \n \n \n\n \n \n Violation Reporting When an architecture rule fails, ts-archunit produces rich violation messages with code frames, file paths, line numbers, and optional context about why the rule exists and how to fix it.\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - TNG/ArchUnit: A Java architecture test library, to specify and ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/TNG/ArchUnit\n \n \n \n \n \n\n \n \n ArchUnit is a free, simple and extensible library for checking the architecture of your Java code. That is, ArchUnit can check dependencies between packages and classes, layers and slices, check for cyclic dependencies and more. It does so by analyzing given Java bytecode, importing all classes into a Java code structure. ArchUnit's main focus is to automatically test architecture and coding ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Unit test your Java architecture - ArchUnit\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.archunit.org\n \n \n \n \n \n\n \n \n ArchUnit is a free, simple and extensible library for checking the architecture of your Java code using any plain Java unit test framework. That is, ArchUnit can check dependencies between packages and classes, layers and slices, check for cyclic dependencies and more.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Introduction to ArchUnit - Baeldung\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.baeldung.com/java-archunit-intro\n \n \n     2025-04-24T00:00:00.0000000\n \n \n \n \n\n \n \n We need to verify that the code implemented adheres to it. Simply put, ArchUnit is a test library that allows us to verify that an application adheres to a given set of architectural rules. But, what is an architectural rule? Even more, what do we mean by architecture in this context? Let's start with the latter.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Testing Java Architecture with ArchUnit: A Comprehensi… | BestHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.besthub.dev/articles/testing-java-architecture-with-archunit-a-comprehensive-guide-eaf4f687a9af\n \n \n     2025-05-29T00:00:00.0000000\n \n \n \n \n\n \n \n Detect and report violations : After importing, write rules with Java 8 lambdas and custom assertions; any class, method or relationship that breaks a rule throws an architecture‑violation exception with a clear message.\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - SimonVerhoeven/archunit-demo: An overview of ArchUnit ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/SimonVerhoeven/archunit-demo\n \n \n \n \n \n\n \n \n Testing your architecture is both an aid to ascertain that the architecture is being implemented consistently and also makes it easier for people onboarding to get a grasp of what that agreed architecture is. One of the advantages of ArchUnit is also that it is "just" another test, and does not need any special infrastructure/new language/...\n \n \n\n \n \n \n \n \n \n \n \n \n \n ArchUnit in practice: Keep your Architecture Clean\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.codecentric.de/en/knowledge-hub/blog/archunit-in-practice-keep-your-architecture-clean\n \n \n \n \n \n\n \n \n It can be easily introduced in both greenfield projects and existing projects. Thanks to the freeze function violations can be fixed step by step and iteratively without blocking the development process. Moreover, ArchUnit fosters team culture by encouraging developers to think about architecture and enforce the decisions made.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=ArchUnit+architecture+test+freeze+violations+report (text/html; charset=UTF-8)","time":{"start":1788985914879,"end":1788985915620}},"id":"prt_g001a087deb3e7001GhHUTPTJS","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de79b4001pFAIFwi1V"}} +{"type":"tool_use","timestamp":1788985915753,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_cfe17f4c612e4ba991a185e6","state":{"status":"completed","input":{"format":"text","url":"https://duckduckgo.com/html/?q=vulture+dead+code+python+static+analysis+unreferenced"},"output":"vulture dead code python static analysis unreferenced at DuckDuckGo\n \n \n \n \n \n \n \n \n \n\n\n\n \n\n \n \n \n\n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n All Regions\n \n Argentina\n \n Australia\n \n Austria\n \n Belgium (fr)\n \n Belgium (nl)\n \n Brazil\n \n Bulgaria\n \n Canada (en)\n \n Canada (fr)\n \n Catalonia\n \n Chile\n \n China\n \n Colombia\n \n Croatia\n \n Czech Republic\n \n Denmark\n \n Estonia\n \n Finland\n \n France\n \n Germany\n \n Greece\n \n Hong Kong\n \n Hungary\n \n Iceland\n \n India (en)\n \n Indonesia (en)\n \n Ireland\n \n Israel (en)\n \n Italy\n \n Japan\n \n Korea\n \n Latvia\n \n Lithuania\n \n Malaysia (en)\n \n Mexico\n \n Netherlands\n \n New Zealand\n \n Norway\n \n Pakistan (en)\n \n Peru\n \n Philippines (en)\n \n Poland\n \n Portugal\n \n Romania\n \n Russia\n \n Saudi Arabia\n \n Singapore\n \n Slovakia\n \n Slovenia\n \n South Africa\n \n Spain (ca)\n \n Spain (es)\n \n Sweden\n \n Switzerland (de)\n \n Switzerland (fr)\n \n Taiwan\n \n Thailand (en)\n \n Turkey\n \n US (English)\n \n US (Spanish)\n \n Ukraine\n \n United Kingdom\n \n Vietnam (en)\n \n \n \n\n \n \n \n Any Time\n \n Past Day\n \n Past Week\n \n Past Month\n \n Past Year\n \n \n \n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n vulture · PyPI\n \n\n \n\n \n \n \n \n \n \n \n \n \n pypi.org/project/vulture/\n \n \n \n \n \n\n \n \n Vulture - Find dead code Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code. Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n GitHub - jendrikseipp/vulture: Find dead Python code\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/jendrikseipp/vulture\n \n \n \n \n \n\n \n \n Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code. Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n Usage Guide | jendrikseipp/vulture | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/jendrikseipp/vulture/3-usage-guide\n \n \n     2025-04-23T00:00:00.0000000\n \n \n \n \n\n \n \n This document provides comprehensive guidance on using Vulture to find dead code in your Python projects. It covers basic and advanced usage patterns, command-line options, and integration methods. For deeper details about Vulture's configuration, see Configuration, or for information about how Vulture detects dead code, see Dead Code Detection.\n \n \n\n \n \n \n \n \n \n \n \n \n \n vulture/README.md at main · jendrikseipp/vulture · GitHub\n \n\n \n\n \n \n \n \n \n \n \n \n \n github.com/jendrikseipp/vulture/blob/main/README.md\n \n \n \n \n \n\n \n \n Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code. Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n vulture - Find dead Python code\n \n\n \n\n \n \n \n \n \n \n \n \n \n libs.tech/project/84045805/vulture\n \n \n \n \n \n\n \n \n Find dead Python code Vulture - Find dead code Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code. Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code. Also, code that is only called implicitly may ...\n \n \n\n \n \n \n \n \n \n \n \n \n \n vulture — Python Dead Code Finder | Open Awesome\n \n\n \n\n \n \n \n \n \n \n \n \n \n open-awesome.com/projects/vulture\n \n \n \n \n \n\n \n \n What is vulture? Vulture is a static analysis tool that finds unused (dead) code in Python programs, including functions, classes, variables, imports, and unreachable code. It helps developers clean up large codebases, identify untested code, and improve overall code quality by highlighting what can be safely removed.\n \n \n\n \n \n \n \n \n \n \n \n \n \n jendrikseipp/vulture | DeepWiki\n \n\n \n\n \n \n \n \n \n \n \n \n \n deepwiki.com/jendrikseipp/vulture\n \n \n     2025-04-23T00:00:00.0000000\n \n \n \n \n\n \n \n Vulture is a static code analysis tool designed to find dead code in Python programs. It identifies unused functions, classes, variables, imports, and unreachable code blocks to help developers clean up their codebases and find potential errors. This document provides an overview of Vulture's architecture, key features, and how it works.\n \n \n\n \n \n \n \n \n \n \n \n \n \n How to Find Dead Code in Python with Vulture - CleanAI Blog\n \n\n \n\n \n \n \n \n \n \n \n \n \n www.cleanai.pro/blog/find-dead-code-python-vulture\n \n \n     2026-02-09T00:00:00.0000000\n \n \n \n \n\n \n \n Vulture detects unused Python code using AST analysis. Learn how to install it, tune confidence thresholds, handle false positives, and integrate with CleanAI.\n \n \n\n \n \n \n \n \n \n \n \n \n \n How can you find unused functions in Python code?\n \n\n \n\n \n \n \n \n \n \n \n \n \n stackoverflow.com/questions/693070/how-can-you-find-unused-functions-in-python-code\n \n \n \n \n \n\n \n \n Alternatively, you can use static code analyzers that just look at your code, but don't actually run it. They run much faster, but due to Python's dynamic nature the results may contain false positives. Two tools in this category are pyflakesand vulture. Pyflakes finds unused imports and unused local variables.\n \n \n\n \n \n \n \n \n \n \n \n \n \n Why Constitutional AI Auditors Miss Dead Code: The Static Analysis vs ...\n \n\n \n\n \n \n \n \n \n \n \n \n \n earezki.com/ai-news/2026-03-10-my-constitutional-auditor-missed-dead-code-heres-why-and-what-im-doing-about-it/\n \n \n     2026-03-10T00:00:00.0000000\n \n \n \n \n\n \n \n Investigation into CORE's constitutional auditor reveals how static analysis tools like Vulture miss orphan files, flagging 231 false positives due to dependency injection.\n \n \n\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n Feedback","metadata":{"truncated":false},"title":"https://duckduckgo.com/html/?q=vulture+dead+code+python+static+analysis+unreferenced (text/html; charset=UTF-8)","time":{"start":1788985914877,"end":1788985915751}},"id":"prt_g001a087deb383001eYv2uqu4s","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087de79b4001pFAIFwi1V"}} +{"type":"step_finish","timestamp":1788985915779,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087deb9680017s5biYkFq","reason":"tool-calls","snapshot":"c6ca66014949990bb20f9a4f92c4101362ac62f3","messageID":"msg_g001a087de79b4001pFAIFwi1V","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":61092,"input":5467,"output":457,"reasoning":0,"cache":{"write":0,"read":55168}},"cost":0}} +{"type":"step_start","timestamp":1788985931498,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087def6e8001AE1RjYaoP","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"27f6e299bb15a25ea92fb14034082275d291ccb8","type":"step-start"}} +{"type":"text","timestamp":1788985932785,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087def940001yuYBIW9hq","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Discovery done — 6 successful queries, 1 empty. I have a strong candidate pool. Now the bounded reading set (4 sources, most relevant to a wiring-truth report generator): O1 import-linter, O4 dependency-cruiser, O5 vulture, O6 ArchUnit.","time":{"start":1788985932096,"end":1788985932782}}} +{"type":"tool_use","timestamp":1788985932996,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_1b324c336ec74cd680c0738e","state":{"status":"error","input":{"format":"text","url":"https://raw.githubusercontent.com/seddonym/import-linter/main/README.rst"},"error":"StatusCode: non 2xx status code (404 GET https://raw.githubusercontent.com/seddonym/import-linter/main/README.rst)","time":{"start":1788985932783,"end":1788985932995}},"id":"prt_g001a087def94a001T7w5nHk3e","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo"}} +{"type":"tool_use","timestamp":1788985933010,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_6c8bf67428e94d88a365fda9","state":{"status":"completed","input":{"format":"text","url":"https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/README.md"},"output":"# Dependency cruiser ![Dependency cruiser](https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/doc/assets/ZKH-Dependency-recolored-160.png)\n\n_Validate and visualise dependencies. With your rules._ JavaScript. TypeScript. CoffeeScript. ES6, CommonJS, AMD.\n\n## What's this do?\n\n![Snazzy dot output to whet your appetite](https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/doc/assets/sample-dot-output.png)\n\nThis runs through the dependencies in any JavaScript, TypeScript, LiveScript or CoffeeScript project and ...\n\n- ... **validates** them against (your own) [rules](./doc/rules-reference.md)\n- ... **reports** violated rules\n - in text (for your builds)\n - in graphics (for your eyeballs)\n\nAs a side effect it can generate dependency graphs in various output formats including [**cool visualizations**](./doc/real-world-samples.md)\nyou can stick on the wall to impress your grandma.\n\n## How do I use it?\n\n### Install it ...\n\n```shell\nnpm install --save-dev dependency-cruiser\n# or\nyarn add -D dependency-cruiser\npnpm add -D dependency-cruiser\n```\n\n### ... and generate a config\n\n```shell\nnpx dependency-cruiser --init\n```\n\nThis will look around in your environment a bit, ask you some questions and create\na `.dependency-cruiser.js` configuration file attuned to your project[^1][^2].\n\n[^1]:\n We're using `npx` in the example scripts for convenience. When you use the\n commands in a script in `package.json` it's not necessary to prefix them with\n `npx`.\n\n[^2]:\n If you don't want to use `npx`, but instead `pnpx` (from the `pnpm`\n package manager) or `yarn` - please refer to that tool's documentation.\n Particularly `pnpx` has semantics that differ from `npx` quite significantly\n and that you want to be aware of before using it. In the mean time: `npx`\n _should_ work even when you installed the dependency with a package manager\n different from `npm`.\n\n### Show stuff to your grandma\n\nTo create a graph of the dependencies in your src folder, you'd run dependency\ncruiser with output type `dot` and run _GraphViz dot_[^3] on the result. In\na one liner:\n\n```shell\nnpx dependency-cruiser src --include-only \"^src\" --output-type dot | dot -T svg > dependency-graph.svg\n```\n\n>
\n> dependency-cruiser v12 and older: add --config option\n>\n> While not necessary from dependency-cruiser v13 and later, in v12 and older\n> you'll have to pass the --config option to make it find the .dependency-cruiser.js\n> configuration file:\n>\n> ```shell\n> npx dependency-cruiser src --include-only \"^src\" --config --output-type dot | dot -T svg > dependency-graph.svg\n> ```\n\n
\n\n- You can read more about what you can do with `--include-only` and other command line\n options in the [command line interface](./doc/cli.md) documentation.\n- _[Real world samples](./doc/real-world-samples.md)_\n contains dependency cruises of some of the most used projects on npm.\n- If your grandma is more into formats like `mermaid`, `json`, `csv`, `html` or plain text\n we've [got her covered](./doc/cli.md#--output-type-specify-the-output-format)\n as well.\n\n[^3]:\n This assumes the GraphViz `dot` command is available - on most linux and\n comparable systems this will be. In case it's not, see\n [GraphViz' download page](https://www.graphviz.org/download/) for instructions\n on how to get it on your machine.\n\n### Validate things\n\n#### Declare some rules\n\nWhen you ran `dependency-cruiser --init` above, the command also added some rules\nto `.dependency-cruiser.js` that make sense in most projects, like detecting\n**circular dependencies**, dependencies **missing** in package.json, **orphans**,\nand production code relying on dev- or optionalDependencies.\n\nStart adding your own rules by tweaking that file.\n\nSample rule:\n\n```json\n{\n \"forbidden\": [\n {\n \"name\": \"not-to-test\",\n \"comment\": \"don't allow dependencies from outside the test folder to test\",\n \"severity\": \"error\",\n \"from\": { \"pathNot\": \"^test\" },\n \"to\": { \"path\": \"^test\" }\n }\n ]\n}\n```\n\n- To read more about writing rules check the\n [writing rules](./doc/rules-tutorial.md) tutorial\n or the [rules reference](./doc/rules-reference.md)\n\n#### Report them\n\n```sh\nnpx dependency-cruiser src\n```\n\n>
\n> dependency-cruiser v12 and older: add --config option\n>\n> While not necessary from dependency-cruiser v13, in v12 and older you'll have\n> to pass the --config option to make it find the .dependency-cruiser.js\n> configuration file:\n>\n> ```shell\n> npx dependency-cruiser --config .dependency-cruiser.js src\n> ```\n\n
\n\nThis will validate against your rules and shows any violations in an eslint-like format:\n\n![sample err output](https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/doc/assets/sample-err-output.png)\n\nThere's more ways to report validations; in a graph (like the one on top of this\nreadme) or in an self-containing `html` file.\n\n- Read more about the err, dot, csv and html reporters in the\n [command line interface](./doc/cli.md)\n documentation.\n- dependency-cruiser uses itself to check on itself in its own build process;\n see the `depcruise` script in the\n [package.json](https://github.com/sverweij/dependency-cruiser/blob/main/package.json#L76)\n\n## I want to know more!\n\nYou've come to the right place :-) :\n\n- Usage\n - [Command line reference](./doc/cli.md)\n - [Writing rules](./doc/rules-tutorial.md)\n - [Rules reference](./doc/rules-reference.md)\n - [Options reference](./doc/options-reference.md)\n - [FAQ](./doc/faq.md)\n- Hacking on dependency-cruiser\n - [API](./doc/api.md)\n - [Output format](./doc/output-format.md)\n - [Adding other output formats](./doc/faq.md#q-how-do-i-add-a-new-output-format)\n - [Adding support for other alt-js languages](./doc/faq.md#q-how-do-i-add-support-for-my-favorite-alt-js-language)\n- Other things\n - [Road map](https://github.com/sverweij/dependency-cruiser/projects/1)\n - [Contact](./doc/faq.md#contact)\n - [Real world show cases](./doc/real-world-samples.md)\n - [TypeScript, CoffeeScript and LiveScript support](./doc/faq.md#features)\n - [Support for .jsx, .tsx, .csx/ .cjsx, .vue and .svelte](./doc/faq.md#q-im-developing-in-react-and-use-jsx-tsx-csx-cjsx-how-do-i-get-that-to-work)\n - [Webpack alias/ modules support](./doc/faq.md#q-does-this-work-with-webpack-configs-eg-alias-and-modules)\n\n## License\n\n[MIT](LICENSE)\n\n## Thanks\n\n- [Marijn Haverbeke](http://marijnhaverbeke.nl) and other people who\n collaborated on [acorn](https://github.com/ternjs/acorn) -\n the excellent JavaScript parser dependency-cruiser uses to infer\n dependencies.\n- [Katerina Limpitsouni](https://twitter.com/ninaLimpi) of [unDraw](https://undraw.co/)\n for the ollie in dependency-cruiser's\n [social media image](https://repository-images.githubusercontent.com/74299372/239ed080-370b-11ea-8fe7-140cf7b90a33).\n- All members of the open source community who have been kind enough to raise issues,\n ask questions and make pull requests to get dependency-cruiser to be a better\n tool.\n\n## Build status\n\n[![GitHub Workflow Status](https://github.com/sverweij/dependency-cruiser/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/sverweij/dependency-cruiser/actions/workflows/ci.yml)\n[![coverage](https://gitlab.com/sverweij/dependency-cruiser/badges/master/coverage.svg)](https://gitlab.com/sverweij/dependency-cruiser/builds)\n[![total downloads on npm](https://img.shields.io/npm/dt/dependency-cruiser.svg?maxAge=2591999)](https://npmjs.com/package/dependency-cruiser)\n\nMade with :metal: in Holland.\n","metadata":{"truncated":false},"title":"https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/README.md (text/plain; charset=utf-8)","time":{"start":1788985932785,"end":1788985933009}},"id":"prt_g001a087def94c001f8p5R237x","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo"}} +{"type":"tool_use","timestamp":1788985933120,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_c3deb92f66414846b6895dbb","state":{"status":"completed","input":{"format":"text","url":"https://www.archunit.org/userguide/html/000_Index.html"},"output":"ArchUnit User Guide\n\n\n\n\n\nArchUnit User Guide\n\nversion 1.5.0\n\n\nTable of Contents\n\n1. Introduction\n\n1.1. Module Overview\n\n\n2. Installation\n\n2.1. JUnit 5 & 6\n2.2. JUnit 4\n2.3. Other Test Frameworks\n2.4. Maven Plugin\n\n\n3. Getting Started\n\n3.1. Importing Classes\n3.2. Asserting (Architectural) Constraints\n3.3. Using JUnit\n3.4. Using JUnit support with Kotlin\n\n\n4. What to Check\n\n4.1. Package Dependency Checks\n4.2. Class Dependency Checks\n4.3. Class and Package Containment Checks\n4.4. Inheritance Checks\n4.5. Annotation Checks\n4.6. Layer Checks\n4.7. Cycle Checks\n\n\n5. Ideas and Concepts\n\n5.1. Core\n5.2. Lang\n5.3. Library\n\n\n6. The Core API\n\n6.1. Import\n6.2. Domain\n\n\n7. The Lang API\n\n7.1. Composing Class Rules\n7.2. Composing Member Rules\n7.3. Creating Custom Rules\n7.4. Predefined Predicates and Conditions\n7.5. Rules with Custom Concepts\n7.6. Controlling the Rule Text\n7.7. Priority\n7.8. Ignoring Violations\n\n\n8. The Library API\n\n8.1. Architectures\n8.2. Slices\n8.3. Modularization Rules\n8.4. General Coding Rules\n8.5. PlantUML Component Diagrams as rules\n8.6. Freezing Arch Rules\n8.7. Software Architecture Metrics\n\n\n9. JUnit Support\n\n9.1. JUnit Integration\n\n\n10. Advanced Configuration\n\n10.1. Overriding configuration\n10.2. Configuring the Resolution Behavior\n10.3. MD5 Sums of Classes\n10.4. Fail Rules on Empty Should\n10.5. Custom Error Messages\n\n\n\n\n\n\n\n1. Introduction\n\n\nArchUnit is a free, simple and extensible library for checking the\narchitecture of your Java code.\nThat is, ArchUnit can check dependencies between packages and classes, layers and slices,\ncheck for cyclic dependencies and more. It does so by analyzing given Java bytecode,\nimporting all classes into a Java code structure.\nArchUnit’s main focus is to automatically test architecture and coding rules,\nusing any plain Java unit testing framework.\n\n\n1.1. Module Overview\n\nArchUnit consists of the following production modules:\n\n\n\n\narchunit\n\n\narchunit-junit4\n\n\narchunit-junit5-api, archunit-junit5-engine, archunit-junit5-engine-api\n\n\narchunit-junit6-api, archunit-junit6-engine, archunit-junit6-engine-api\n\n\n\n\nThe archunit-example is also relevant to end users.\n\n\n1.1.1. Module archunit\n\nThis module contains the actual ArchUnit core infrastructure required to write architecture\ntests: The ClassFileImporter,\nthe domain objects, as well as the rule syntax infrastructure.\n\n\n\n1.1.2. Module archunit-junit4\n\nThis module contains the infrastructure to integrate with JUnit 4, in particular\nthe ArchUnitRunner to cache imported classes.\n\n\n\n1.1.3. Modules archunit-junit*-*\n\nThese modules contain the infrastructure to integrate with JUnit 5 & 6 and contain the respective\ninfrastructure to cache imported classes between test runs.\narchunit-junit*-api contains the user API to write tests with ArchUnit’s JUnit support.\narchunit-junit*-engine contains the runtime engine to run those tests.\narchunit-junit*-engine-api contains API code for tools that want more detailed control\nover running ArchUnit JUnit tests, in particular a FieldSelector which can be used to\ninstruct the ArchUnitTestEngine to run a specific rule field (compare JUnit Integration).\n\n\n\n1.1.4. Module archunit-example\n\nThis module contains example architecture rules and sample code that violates these rules.\nLook here to get inspiration on how to set up rules for your project, or at\nArchUnit-Examples for the last released version.\n\n\n\n\n\n\n2. Installation\n\n\nTo use ArchUnit, it is sufficient to include the respective JAR files in the classpath.\nMost commonly, this is done by adding the dependency to your dependency management tool,\nwhich is illustrated for Maven and Gradle below. Alternatively, you\ncan obtain the necessary JAR files directly from\nMaven Central.\n\n\n2.1. JUnit 5 & 6\n\nArchUnit’s JUnit 5 & 6 artifacts follow the pattern of JUnit Jupiter. There is one artifact containing\nthe API, i.e. the compile time dependencies to write tests. Then there is another artifact containing\nthe actual TestEngine used at runtime. Just like JUnit Jupiter, ArchUnit offers one convenience\nartifact transitively including both API and engine with the correct scope, which in turn can be added\nas a test compile dependency. Thus, to include ArchUnit’s JUnit 5 & 6 support, simply add the following dependency\nfrom Maven Central:\n\n\nbuild.gradle\n\ndependencies {\n // testImplementation 'com.tngtech.archunit:archunit-junit5:1.5.0' // for JUnit 5\n testImplementation 'com.tngtech.archunit:archunit-junit6:1.5.0'\n}\n\n\n\npom.xml\n\n<dependency>\n <groupId>com.tngtech.archunit</groupId>\n <!-- <artifactId>archunit-junit5</artifactId> --> <!-- for JUnit 5 -->\n <artifactId>archunit-junit6</artifactId>\n <version>1.5.0</version>\n <scope>test</scope>\n</dependency>\n\n\n\n\n2.2. JUnit 4\n\nTo use ArchUnit in combination with JUnit 4, include the following dependency from\nMaven Central:\n\n\nbuild.gradle\n\ndependencies {\n testImplementation 'com.tngtech.archunit:archunit-junit4:1.5.0'\n}\n\n\n\npom.xml\n\n<dependency>\n <groupId>com.tngtech.archunit</groupId>\n <artifactId>archunit-junit4</artifactId>\n <version>1.5.0</version>\n <scope>test</scope>\n</dependency>\n\n\n\n\n2.3. Other Test Frameworks\n\nArchUnit works with any test framework that executes Java code. To use ArchUnit in such a\ncontext, include the core ArchUnit dependency from Maven Central:\n\n\nbuild.gradle\n\ndependencies {\n testImplementation 'com.tngtech.archunit:archunit:1.5.0'\n}\n\n\n\npom.xml\n\n<dependency>\n <groupId>com.tngtech.archunit</groupId>\n <artifactId>archunit</artifactId>\n <version>1.5.0</version>\n <scope>test</scope>\n</dependency>\n\n\n\n\n2.4. Maven Plugin\n\nThere exists a Maven plugin by Société Générale to run ArchUnit rules straight from Maven. For\nmore information visit their GitHub repo: https://github.com/societe-generale/arch-unit-maven-plugin\n\n\n\n\n\n3. Getting Started\n\n\nArchUnit tests are written the same way as any Java unit test and can be written with any\nJava unit testing framework. To really understand the ideas behind ArchUnit, one should consult\nIdeas and Concepts. The following will outline a \"technical\" getting started.\n\n\n3.1. Importing Classes\n\nAt its core, ArchUnit provides infrastructure to import Java bytecode into Java code structures.\nThis can be done using the ClassFileImporter:\n\n\n\nJavaClasses classes = new ClassFileImporter().importPackages(\"com.mycompany.myapp\");\n\n\n\nThe ClassFileImporter offers many ways to import classes. Some ways depend on\nthe current project’s classpath, like importPackages(..). However there are other ways\nthat do not, for example:\n\n\n\nJavaClasses classes = new ClassFileImporter().importPath(\"/some/path\");\n\n\n\nThe returned object of type JavaClasses represents a collection of elements of type\nJavaClass, where JavaClass in turn represents a single imported class file. You can\nin fact access most properties of the imported class via the public API:\n\n\n\nJavaClass clazz = classes.get(Object.class);\nSystem.out.print(clazz.getSimpleName()); // returns 'Object'\n\n\n\n\n3.2. Asserting (Architectural) Constraints\n\nTo express architectural rules, like 'Services should only be accessed by Controllers',\nArchUnit offers an abstract DSL-like fluent API, which can in turn be evaluated against\nimported classes. To specify a rule, use the class ArchRuleDefinition as entry point:\n\n\n\nimport static com.tngtech.archunit.lang.syntax.ArchRuleDefinition.classes;\n\n// ...\n\nArchRule myRule = classes()\n .that().resideInAPackage(\"..service..\")\n .should().onlyBeAccessed().byAnyPackage(\"..controller..\", \"..service..\");\n\n\n\nThe two dots represent any number of packages (compare AspectJ Pointcuts); for details on\nthe supported package pattern syntax see Package Identifiers. The returned object of\ntype ArchRule can now be evaluated against a set of imported classes:\n\n\n\nmyRule.check(importedClasses);\n\n\n\nThus the complete example could look like\n\n\n\n@Test\npublic void Services_should_only_be_accessed_by_Controllers() {\n JavaClasses importedClasses = new ClassFileImporter().importPackages(\"com.mycompany.myapp\");\n\n ArchRule myRule = classes()\n .that().resideInAPackage(\"..service..\")\n .should().onlyBeAccessed().byAnyPackage(\"..controller..\", \"..service..\");\n\n myRule.check(importedClasses);\n}\n\n\n\n\n3.3. Using JUnit\n\nWhile ArchUnit can be used with any unit testing framework, it provides extended support\nfor writing tests with JUnit. The main advantage is automatic caching of imported\nclasses between tests (of the same imported classes), as well as reduction of boilerplate code.\n\n\nTo use the JUnit support, declare ArchUnit’s ArchUnitRunner (only JUnit 4), declare the classes\nto import via @AnalyzeClasses and add the respective rules as fields:\n\n\n\n// @RunWith(ArchUnitRunner.class) // Enable this line for JUnit 4\n@AnalyzeClasses(packages = \"com.mycompany.myapp\")\npublic class MyArchitectureTest {\n\n @ArchTest\n public static final ArchRule myRule = classes()\n .that().resideInAPackage(\"..service..\")\n .should().onlyBeAccessed().byAnyPackage(\"..controller..\", \"..service..\");\n\n}\n\n\n\nThe JUnit test support will automatically import (or reuse) the specified classes and\nevaluate any rule annotated with @ArchTest against those classes.\n\n\nFor further information on how to use the JUnit support refer to JUnit Support.\n\n\n\n3.4. Using JUnit support with Kotlin\n\nUsing the JUnit support with Kotlin is quite similar to Java:\n\n\n\n// @RunWith(ArchUnitRunner::class) // Enable this line for JUnit 4\n@AnalyzeClasses(packagesOf = [MyArchitectureTest::class])\nclass MyArchitectureTest {\n @ArchTest\n val rule_as_field = ArchRuleDefinition.noClasses().should()...\n\n @ArchTest\n fun rule_as_method(importedClasses: JavaClasses) {\n val rule = ArchRuleDefinition.noClasses().should()...\n rule.check(importedClasses)\n }\n}\n\n\n\n\n\n\n4. What to Check\n\n\nThe following section illustrates some typical checks you could do with ArchUnit.\n\n\n4.1. Package Dependency Checks\n\n\npackage deps no access\n\n\n\n\nnoClasses().that().resideInAPackage(\"..source..\")\n .should().dependOnClassesThat().resideInAPackage(\"..foo..\")\n\n\n\n\npackage deps only access\n\n\n\n\nclasses().that().resideInAPackage(\"..foo..\")\n .should().onlyHaveDependentClassesThat().resideInAnyPackage(\"..source.one..\", \"..foo..\")\n\n\n\n\n4.2. Class Dependency Checks\n\n\nclass naming deps\n\n\n\n\nclasses().that().haveNameMatching(\".*Bar\")\n .should().onlyHaveDependentClassesThat().haveSimpleName(\"Bar\")\n\n\n\n\n4.3. Class and Package Containment Checks\n\n\nclass package contain\n\n\n\n\nclasses().that().haveSimpleNameStartingWith(\"Foo\")\n .should().resideInAPackage(\"com.foo\")\n\n\n\n\n4.4. Inheritance Checks\n\n\ninheritance naming check\n\n\n\n\nclasses().that().implement(Connection.class)\n .should().haveSimpleNameEndingWith(\"Connection\")\n\n\n\n\ninheritance access check\n\n\n\n\nclasses().that().areAssignableTo(EntityManager.class)\n .should().onlyHaveDependentClassesThat().resideInAnyPackage(\"..persistence..\")\n\n\n\n\n4.5. Annotation Checks\n\n\ninheritance annotation check\n\n\n\n\nclasses().that().areAssignableTo(EntityManager.class)\n .should().onlyHaveDependentClassesThat().areAnnotatedWith(Transactional.class)\n\n\n\n\n4.6. Layer Checks\n\n\nlayer check\n\n\n\n\nlayeredArchitecture()\n .consideringAllDependencies()\n .layer(\"Controller\").definedBy(\"..controller..\")\n .layer(\"Service\").definedBy(\"..service..\")\n .layer(\"Persistence\").definedBy(\"..persistence..\")\n\n .whereLayer(\"Controller\").mayNotBeAccessedByAnyLayer()\n .whereLayer(\"Service\").mayOnlyBeAccessedByLayers(\"Controller\")\n .whereLayer(\"Persistence\").mayOnlyBeAccessedByLayers(\"Service\")\n\n\n\n\n4.7. Cycle Checks\n\n\ncycle check\n\n\n\n\nslices().matching(\"com.myapp.(*)..\").should().beFreeOfCycles()\n\n\n\n\n\n\n5. Ideas and Concepts\n\n\nArchUnit is divided into different layers, where the most important ones are the \"Core\" layer,\nthe \"Lang\" layer and the \"Library\" layer. In short the Core layer deals with the basic\ninfrastructure, i.e. how to import byte code into Java objects. The Lang layer contains the\nrule syntax to specify architecture rules in a succinct way. The Library layer contains\nmore complex predefined rules, like a layered architecture with several layers. The following\nsection will explain these layers in more detail.\n\n\n5.1. Core\n\nMuch of ArchUnit’s core API resembles the Java Reflection API.\nThere are classes like JavaMethod, JavaField, and more,\nand the public API consists of methods like getName(), getMethods(),\ngetRawType() or getRawParameterTypes().\nAdditionally ArchUnit extends this API for concepts needed to talk about dependencies between code,\nlike JavaMethodCall, JavaConstructorCall or JavaFieldAccess.\nFor example, it is possible to programmatically iterate over javaClass.getAccessesFromSelf()\nand react to the imported accesses between this Java class and other Java classes.\n\n\nTo import compiled Java class files, ArchUnit provides the ClassFileImporter, which can\nfor example be used to import packages from the classpath:\n\n\n\nJavaClasses classes = new ClassFileImporter().importPackages(\"com.mycompany.myapp\");\n\n\n\nFor more information refer to The Core API.\n\n\n\n5.2. Lang\n\nThe Core API is quite powerful and offers a lot of information about the static structure\nof a Java program. However, tests directly using the Core API lack expressiveness,\nin particular with respect to architectural rules.\n\n\nFor this reason ArchUnit provides the Lang API, which offers a powerful syntax to express rules\nin an abstract way. Most parts of the Lang API are composed as fluent APIs, i.e. an IDE can\nprovide valuable suggestions on the possibilities the syntax offers.\n\n\nAn example for a specified architecture rule would be:\n\n\n\nArchRule rule =\n classes().that().resideInAPackage(\"..service..\")\n .should().onlyBeAccessed().byAnyPackage(\"..controller..\", \"..service..\");\n\n\n\nOnce a rule is composed, imported Java classes can be checked against it:\n\n\n\nJavaClasses importedClasses = new ClassFileImporter().importPackage(\"com.myapp\");\nArchRule rule = // define the rule\nrule.check(importedClasses);\n\n\n\nThe syntax ArchUnit provides is fully extensible and can thus be adjusted to almost any\nspecific need. For further information, please refer to The Lang API.\n\n\n\n5.3. Library\n\nThe Library API offers predefined complex rules for typical architectural goals. For example\na succinct definition of a layered architecture via package definitions. Or rules to slice\nthe code base in a certain way, for example in different areas of the domain, and enforce these\nslices to be acyclic or independent of each other. More detailed information is provided in\nThe Library API.\n\n\n\n\n\n6. The Core API\n\n\nThe Core API is itself divided into the domain objects and the actual import.\n\n\n6.1. Import\n\nAs mentioned in Ideas and Concepts the backbone of the infrastructure is the ClassFileImporter,\nwhich provides various ways to import Java classes. One way is to import packages from\nthe classpath, or the complete classpath via\n\n\n\nJavaClasses classes = new ClassFileImporter().importClasspath();\n\n\n\nHowever, the import process is completely independent of the classpath, so it would be well possible\nto import any path from the file system:\n\n\n\nJavaClasses classes = new ClassFileImporter().importPath(\"/some/path/to/classes\");\n\n\n\nThe ClassFileImporter offers several other methods to import classes, for example locations can be\nspecified as URLs or as JAR files.\n\n\nFurthermore specific locations can be filtered out, if they are contained in the source of classes,\nbut should not be imported. A typical use case would be to ignore test classes, when the classpath\nis imported. This can be achieved by specifying ImportOptions:\n\n\n\nImportOption ignoreTests = new ImportOption() {\n @Override\n public boolean includes(Location location) {\n return !location.contains(\"/test/\"); // ignore any URI to sources that contains '/test/'\n }\n};\n\nJavaClasses classes = new ClassFileImporter().withImportOption(ignoreTests).importClasspath();\n\n\n\nA Location is principally an URI, i.e. ArchUnit considers sources as File or JAR URIs\n\n\n\n\nfile:///home/dev/my/project/target/classes/some/Thing.class\n\n\njar:file:///home/dev/.m2/repository/some/things.jar!/some/Thing.class\n\n\n\n\nFor the two common cases to skip importing JAR files and to skip importing test files\n(for typical setups, like a Maven or Gradle build),\nthere already exist predefined ImportOptions:\n\n\n\nnew ClassFileImporter()\n .withImportOption(ImportOption.Predefined.DO_NOT_INCLUDE_JARS)\n .withImportOption(ImportOption.Predefined.DO_NOT_INCLUDE_TESTS)\n .importClasspath();\n\n\n\n6.1.1. Dealing with Missing Classes\n\nWhile importing the requested classes (e.g. target/classes or target/test-classes)\nit can happen that a class within the scope of the import has a reference to a class outside of the\nscope of the import. This will naturally happen, if the classes of the JDK are not imported,\nsince then for example any dependency on Object.class will be unresolved within the import.\n\n\nAt this point ArchUnit needs to decide how to treat these classes that are missing from the\nimport. By default, ArchUnit searches within the classpath for missing classes and if found\nimports them. This obviously has the advantage that information about those classes\n(which interfaces they implement, how they are annotated) is present during rule evaluation.\n\n\nOn the downside this additional lookup from the classpath will cost some performance and in some\ncases might not make sense (e.g. if information about classes not present in the original import\nis known to be unnecessary for evaluating rules).\nThus ArchUnit can be configured to create stubs instead, i.e. a JavaClass that has all the known\ninformation, like the fully qualified name or the method called. However, this stub might\nnaturally lack some information, like superclasses, annotations or other details that cannot\nbe determined without importing the bytecode of this class. This behavior will also happen,\nif ArchUnit fails to determine the location of a missing class from the classpath.\n\n\nTo find out, how to configure the default behavior, refer to Configuring the Resolution Behavior.\n\n\n\n\n6.2. Domain\n\nThe domain objects represent Java code, thus the naming should be pretty straight forward. Most\ncommonly, the ClassFileImporter imports instances of type JavaClass. A rough overview looks\nlike this:\n\n\n\ndomain overview\n\n\n\nMost objects resemble the Java Reflection API, including inheritance relations. Thus a JavaClass\nhas JavaMembers, which can in turn be either JavaField, JavaMethod,\nJavaConstructor (or JavaStaticInitializer). While not present within the reflection API,\nit makes sense to introduce an expression for anything that can access other code, which ArchUnit\ncalls 'code unit', and is in fact either a method, a constructor (including the class initializer)\nor a static initializer of a class (e.g. a static { …​ } block, a static field assignment,\netc.).\n\n\nFurthermore one of the most interesting features of ArchUnit that exceeds the Java Reflection API,\nis the concept of accesses to another class. On the lowest level accesses can only take place\nfrom a code unit (as mentioned, any block of executable code) to either a field (JavaFieldAccess),\na method (JavaMethodCall) or constructor (JavaConstructorCall).\n\n\nArchUnit imports the whole graph of classes and their relationship to each other. While checking\nthe accesses from a class is pretty isolated (the bytecode offers all this information),\nchecking accesses to a class requires the whole graph to be built first. To distinguish which\nsort of access is referred to, methods will always clearly state fromSelf and toSelf.\nFor example, every JavaField allows to call JavaField#getAccessesToSelf() to retrieve all\ncode units within the graph that access this specific field. The resolution process through\ninheritance is not completely straight forward. Consider for example\n\n\n\nresolution example\n\n\n\nThe bytecode will record a field access from ClassAccessing.accessField() to\nClassBeingAccessed.accessedField. However, there is no such field, since the field is\nactually declared in the superclass. This is the reason why a JavaFieldAccess\nhas no JavaField as its target, but a FieldAccessTarget. In other words, ArchUnit models\nthe situation, as it is found within the bytecode, and an access target is not an actual\nmember within another class. If a member is queried for accessesToSelf() though, ArchUnit\nwill resolve the necessary targets and determine, which member is represented by which target.\nThe situation looks roughly like\n\n\n\nresolution overview\n\n\n\nTwo things might seem strange at the first look.\n\n\nFirst, why can a target resolve to zero matching members? The reason is that the set of classes\nthat was imported does not need to have all classes involved within this resolution process.\nConsider the above example, if SuperclassBeingAccessed would not be imported, ArchUnit would\nhave no way of knowing where the actual targeted field resides. Thus in this case the\nresolution would return zero elements.\n\n\nSecond, why can there be more than one resolved methods for method calls?\nThe reason for this is that a call target might indeed match several methods in those\ncases, for example:\n\n\n\ndiamond example\n\n\n\nWhile this situation will always be resolved in a specified way for a real program,\nArchUnit cannot do the same. Instead, the resolution will report all candidates that match a\nspecific access target, so in the above example, the call target C.targetMethod() would in fact\nresolve to two JavaMethods, namely A.targetMethod() and B.targetMethod(). Likewise a check\nof either A.targetMethod.getCallsToSelf() or B.targetMethod.getCallsToSelf() would return\nthe same call from D.callTargetMethod() to C.targetMethod().\n\n\n6.2.1. Domain Objects, Reflection and the Classpath\n\nArchUnit tries to offer a lot of information from the bytecode. For example, a JavaClass\nprovides details like if it is an enum or an interface, modifiers like public or abstract,\nbut also the source, where this class was imported from (namely the URI mentioned in the first\nsection). However, if information is missing, and the classpath is correct, ArchUnit offers\nsome convenience to rely on the reflection API for extended details. For this reason, most\nJava* objects offer a method reflect(), which will in fact try to resolve the respective\nobject from the Reflection API. For example:\n\n\n\nJavaClasses classes = new ClassFileImporter().importClasspath();\n\n// ArchUnit's java.lang.String\nJavaClass javaClass = classes.get(String.class);\n// Reflection API's java.lang.String\nClass<?> stringClass = javaClass.reflect();\n\n// ArchUnit's public int java.lang.String.length()\nJavaMethod javaMethod = javaClass.getMethod(\"length\");\n// Reflection API's public int java.lang.String.length()\nMethod lengthMethod = javaMethod.reflect();\n\n\n\nHowever, this will throw an Exception, if the respective classes are missing on the classpath\n(e.g. because they were just imported from some file path).\n\n\nThis restriction also applies to handling annotations in a more convenient way.\nConsider the following annotation:\n\n\n\n@interface CustomAnnotation {\n String value();\n}\n\n\n\nIf you need to access this annotation without it being on the classpath, you must rely on\n\n\n\nJavaAnnotation<?> annotation = javaClass.getAnnotationOfType(\"some.pkg.CustomAnnotation\");\n// result is untyped, since it might not be on the classpath (e.g. enums)\nObject value = annotation.get(\"value\");\n\n\n\nSo there is neither type safety nor automatic refactoring support. If this annotation is on the classpath, however,\nthis can be written way more naturally:\n\n\n\nCustomAnnotation annotation = javaClass.getAnnotationOfType(CustomAnnotation.class);\nString value = annotation.value();\n\n\n\nArchUnit’s own rule APIs (compare The Lang API) never rely on the\nclasspath though. Thus the evaluation of default rules and syntax combinations, described in the\nnext section, does not depend on whether the classes were imported from the classpath or\nsome JAR / folder.\n\n\n\n6.2.2. Package Identifiers\n\nSeveral ArchUnit methods accept a String package identifier to describe a set of packages,\nfor example resideInAPackage(..) and resideInAnyPackage(..) (and their negated variants)\nin the Lang API, slices().matching(..) and modules().definedByPackages(..) in the Library\nAPI, or the component stereotypes of a PlantUML component diagram. All of them delegate to\nthe same underlying PackageMatcher, whose syntax is inspired by AspectJ type patterns and\nextended with capturing groups.\n\n\nWildcards\n\nThe syntax is built from the following elements:\n\n\n\n\n\n\n\n\nPattern\nMeaning\n\n\n\n\n*\nMatches any non-empty sequence of characters not containing the dot ., i.e. exactly one package segment.\n\n\n..\nMatches any (possibly empty) sequence of characters that may contain the dot ., i.e. any number of package segments (including zero).\n\n\n(*)\nLike *, but additionally captures the matched segment, so it can be referenced later (e.g. as a slice identifier, see Slices).\n\n\n(**)\nLike .., but additionally captures the matched segments.\n\n\n[a|b]\nAlternation: matches either a or b. Alternations are only allowed inside brackets [...] or inside capturing groups (...).\n\n\n\n\nNote that (..) is not a valid capturing group — use (**) instead. () and [] cannot\nbe nested inside each other.\n\n\nThe segments matched by capturing groups can be retrieved from the result of\nPackageMatcher#match(String)\nvia\nPackageMatcher.Result#getGroup(int)\n(groups are 1-based, in the order the capturing groups appear in the pattern).\n\n\n\nWorked Example\n\nConsider the following five classes and the packages they reside in:\n\n\n\n\n\n\n\n\nClass\nPackage\n\n\n\n\ncom.myapp.controller.SomeController\ncom.myapp.controller\n\n\ncom.myapp.service.SomeService\ncom.myapp.service\n\n\ncom.myapp.service.impl.SomeServiceImpl\ncom.myapp.service.impl\n\n\ncom.myapp.persistence.dao.SomeDao\ncom.myapp.persistence.dao\n\n\ncom.myapp.persistence.dao.jpa.SomeJpa\ncom.myapp.persistence.dao.jpa\n\n\n\n\nThe following table shows a range of package identifiers and which of the five packages\nabove each of them matches:\n\n\n\n\n\n\n\n\nPackage Identifier\nMatches\n\n\n\n\ncom.myapp.service\ncom.myapp.service only (exact match).\n\n\ncom.myapp.*\ncom.myapp.controller and com.myapp.service\n(exactly one more segment after com.myapp).\n\n\ncom.myapp..\nAll five packages — com.myapp.. matches com.myapp itself and every subpackage of it.\n\n\n..service\ncom.myapp.service only (segment service at the end).\n\n\n..service..\ncom.myapp.service and com.myapp.service.impl\n(any package containing a segment service).\n\n\n..dao..\ncom.myapp.persistence.dao and com.myapp.persistence.dao.jpa.\n\n\n..impl\ncom.myapp.service.impl only.\n\n\ncom.myapp.(*)\ncom.myapp.controller (group 1 = controller) and\ncom.myapp.service (group 1 = service).\nThe deeper packages do not match because (*) allows only a single segment.\n\n\ncom.myapp.(*)..\nAll five packages; group 1 captures the first sub-package below com.myapp\n(controller, service, service, persistence, persistence respectively).\nThis is the typical pattern used by slices().matching(..).\n\n\n..[service|controller]..\ncom.myapp.controller, com.myapp.service and com.myapp.service.impl.\n\n\n\n\n\nWhat is Matched Against the Pattern\n\nAn important detail is that the package identifier is matched against the\npackage name of a class, not against the fully qualified class name.\nSo for com.myapp.service.SomeService the string that is checked against\nthe pattern is com.myapp.service, not com.myapp.service.SomeService.\n\n\nThis means that a pattern like ..SomeService will not match a class named SomeService,\nbecause SomeService is the simple class name, not a package segment. To match by class\nname use a name-based predicate such as\nhaveSimpleName(\"SomeService\") or haveNameMatching(\".*SomeService\") instead.\n\n\n\n\n\n\n\n7. The Lang API\n\n\n7.1. Composing Class Rules\n\nThe Core API is pretty powerful with regard to all the details from the bytecode\nthat it provides to tests. However, tests written this way lack conciseness and fail to convey the\narchitectural concept that they should assert. Consider:\n\n\n\nSet<JavaClass> services = new HashSet<>();\nfor (JavaClass clazz : classes) {\n // choose those classes with FQN with infix '.service.'\n if (clazz.getName().contains(\".service.\")) {\n services.add(clazz);\n }\n}\n\nfor (JavaClass service : services) {\n for (JavaAccess<?> access : service.getAccessesFromSelf()) {\n String targetName = access.getTargetOwner().getName();\n\n // fail if the target FQN has the infix \".controller.\"\n if (targetName.contains(\".controller.\")) {\n String message = String.format(\n \"Service %s accesses Controller %s in line %d\",\n service.getName(), targetName, access.getLineNumber());\n Assert.fail(message);\n }\n }\n}\n\n\n\nWhat we want to express, is the rule \"no classes that reside in a package 'service' should\naccess classes that reside in a package 'controller'\". Nevertheless, it’s hard to read through\nthat code and distill that information. And the same process has to be done every time someone\nneeds to understand the semantics of this rule.\n\n\nTo solve this shortcoming, ArchUnit offers a high level API to express architectural concepts\nin a concise way. In fact, we can write code that is almost equivalent to the prose rule text\nmentioned before:\n\n\n\nArchRule rule = ArchRuleDefinition.noClasses()\n .that().resideInAPackage(\"..service..\")\n .should().accessClassesThat().resideInAPackage(\"..controller..\");\n\nrule.check(importedClasses);\n\n\n\nThe only difference to colloquial language is the \"..\" in the package notation,\nwhich refers to any number of packages. Thus \"..service..\" just expresses\n\"any package that contains some sub-package 'service'\", e.g. com.myapp.service.any.\nIf this test fails, it will report an AssertionError with the following message:\n\n\n\njava.lang.AssertionError: Architecture Violation [Priority: MEDIUM] -\nRule 'no classes that reside in a package '..service..'\nshould access classes that reside in a package '..controller..'' was violated (1 times):\nMethod <some.pkg.service.SomeService.callController()>\ncalls method <some.pkg.controller.SomeController.execute()>\nin (SomeService.java:14)\n\n\n\nSo as a benefit, the assertion error contains the full rule text out of the box and reports\nall violations including the exact class and line number. The rule API also allows to combine\npredicates and conditions:\n\n\n\nnoClasses()\n .that().resideInAPackage(\"..service..\")\n .or().resideInAPackage(\"..persistence..\")\n .should().accessClassesThat().resideInAPackage(\"..controller..\")\n .orShould().accessClassesThat().resideInAPackage(\"..ui..\")\n\nrule.check(importedClasses);\n\n\n\n\n7.2. Composing Member Rules\n\nIn addition to a predefined API to write rules about Java classes and their relations, there is\nan extended API to define rules for members of Java classes. This might be relevant, for example,\nif methods in a certain context need to be annotated with a specific annotation, or return\ntypes implementing a certain interface. The entry point is again ArchRuleDefinition, e.g.\n\n\n\nArchRule rule = ArchRuleDefinition.methods()\n .that().arePublic()\n .and().areDeclaredInClassesThat().resideInAPackage(\"..controller..\")\n .should().beAnnotatedWith(Secured.class);\n\nrule.check(importedClasses);\n\n\n\nBesides methods(), ArchRuleDefinition offers the methods members(), fields(), codeUnits(), constructors()\n– and the corresponding negations noMembers(), noFields(), noMethods(), etc.\n\n\n\n7.3. Creating Custom Rules\n\nIn fact, most architectural rules take the form\n\n\n\nclasses that ${PREDICATE} should ${CONDITION}\n\n\n\nIn other words, we always want to limit imported classes to a relevant subset,\nand then evaluate some condition to see that all those classes satisfy it.\nArchUnit’s API allows you to do just that, by exposing the concepts of DescribedPredicate and ArchCondition.\nSo the rule above is just an application of this generic API:\n\n\n\nDescribedPredicate<JavaClass> resideInAPackageService = // define the predicate\nArchCondition<JavaClass> accessClassesThatResideInAPackageController = // define the condition\n\nnoClasses().that(resideInAPackageService)\n .should(accessClassesThatResideInAPackageController);\n\n\n\nThus, if the predefined API does not allow to express some concept,\nit is possible to extend it in any custom way.\nFor example:\n\n\n\nDescribedPredicate<JavaClass> haveAFieldAnnotatedWithPayload =\n new DescribedPredicate<JavaClass>(\"have a field annotated with @Payload\"){\n @Override\n public boolean test(JavaClass input) {\n boolean someFieldAnnotatedWithPayload = // iterate fields and check for @Payload\n return someFieldAnnotatedWithPayload;\n }\n };\n\nArchCondition<JavaClass> onlyBeAccessedBySecuredMethods =\n new ArchCondition<JavaClass>(\"only be accessed by @Secured methods\") {\n @Override\n public void check(JavaClass item, ConditionEvents events) {\n for (JavaMethodCall call : item.getMethodCallsToSelf()) {\n if (!call.getOrigin().isAnnotatedWith(Secured.class)) {\n String message = String.format(\n \"Method %s is not @Secured\", call.getOrigin().getFullName());\n events.add(SimpleConditionEvent.violated(call, message));\n }\n }\n }\n };\n\nclasses().that(haveAFieldAnnotatedWithPayload).should(onlyBeAccessedBySecuredMethods);\n\n\n\nIf the rule fails, the error message will be built from the supplied descriptions. In the\nexample above, it would be\n\n\n\nclasses that have a field annotated with @Payload should only be accessed by @Secured methods\n\n\n\n\n7.4. Predefined Predicates and Conditions\n\nCustom predicates and conditions like in the last section can often be composed from predefined elements.\nArchUnit’s basic convention for predicates is that they are defined in an inner class Predicates within the type they target.\nFor example, one can find the predicate to check for the simple name of a JavaClass as\n\n\n\nJavaClass.Predicates.simpleName(String)\n\n\n\nPredicates can be joined using the methods predicate.or(other) and predicate.and(other).\nSo for example a predicate testing for a class with simple name \"Foo\" that is serializable\ncould be created the following way:\n\n\n\nimport static com.tngtech.archunit.core.domain.JavaClass.Predicates.assignableTo;\nimport static com.tngtech.archunit.core.domain.JavaClass.Predicates.simpleName;\n\nDescribedPredicate<JavaClass> serializableNamedFoo =\n simpleName(\"Foo\").and(assignableTo(Serializable.class));\n\n\n\nNote that for some properties, there exist interfaces with predicates defined for them.\nFor example the property to have a name is represented by the interface HasName;\nconsequently the predicate to check the name of a JavaClass\nis the same as the predicate to check the name of a JavaMethod,\nand resides within\n\n\n\nHasName.Predicates.name(String)\n\n\n\nThis can at times lead to problems with the type system, if predicates are supposed to be joined.\nSince the or(..) method accepts a type of DescribedPredicate<? super T>,\nwhere T is the type of the first predicate. For example:\n\n\n\n// Does not compile, because type(..) targets a subtype of HasName\nHasName.Predicates.name(\"\").and(JavaClass.Predicates.type(Serializable.class))\n\n// Does compile, because name(..) targets a supertype of JavaClass\nJavaClass.Predicates.type(Serializable.class).and(HasName.Predicates.name(\"\"))\n\n\n... 1114 lines omitted — showing head and tail ...\n\n }\n\n}\n\n\n\nThe JavaClass cache will work in two ways. On the one hand it will cache the classes by test,\nso they can be reused by several rules declared within the same class. On the other hand, it\nwill cache the classes by location, so a second test that wants to import classes from the same\nURLs will reuse the classes previously imported as well. Note that this second caching uses\nsoft references, so the classes will be dropped from memory, if the heap runs low.\nFor further information see Controlling the Cache.\n\n\n\n9.1.2. Controlling the Import\n\nWhich classes will be imported can be controlled in a declarative way through @AnalyzeClasses.\nIf no packages or locations are provided, the package of the annotated test class will be imported.\nYou can specify packages to import as strings:\n\n\n\n@AnalyzeClasses(packages = {\"com.myapp.subone\", \"com.myapp.subtwo\"})\n\n\n\nTo better support refactorings, packages can also be declared relative to classes, i.e. the\npackages these classes reside in will be imported:\n\n\n\n@AnalyzeClasses(packagesOf = {SubOneConfiguration.class, SubTwoConfiguration.class})\n\n\n\nAs an alternative to specifying packages, you can directly specify individual classes to be analyzed:\n\n\n\n@AnalyzeClasses(classes = {String.class, Integer.class})\n\n\n\nThis allows for more fine-grained control over which classes are imported for testing.\nYou can combine the classes property with other properties like packages, packagesOf, etc.\nIn this case, all specified classes and packages will be imported for analysis.\n\n\nAs another option, locations can be specified freely by implementing a LocationProvider:\n\n\n\npublic class MyLocationProvider implements LocationProvider {\n @Override\n public Set<Location> get(Class<?> testClass) {\n // Determine Locations (= URLs) to import\n // Can also consider the actual test class, e.g. to read some custom annotation\n }\n}\n\n@AnalyzeClasses(locations = MyLocationProvider.class)\n\n\n\nFurthermore, in order to choose specific classes beneath those locations, ImportOptions can be\nspecified (compare The Core API). The following example imports the classpath, but only considers\nproduction code and only considers code that is directly supplied and does not come from JARs:\n\n\n\n@AnalyzeClasses(importOptions = {DoNotIncludeTests.class, DoNotIncludeJars.class})\n\n\n\nAs explained in The Core API, you can write your own custom implementation of ImportOption\nand then supply the type to @AnalyzeClasses.\n\n\nTo import the whole classpath, instead of just the package of the test class, use the option\n\n\n\n@AnalyzeClasses(wholeClasspath = true)\n\n\n\nNote that @AnalyzeClasses can also be used as a meta-annotation to avoid repeating the same configuration:\n\n\n\n@Retention(RetentionPolicy.RUNTIME)\n@AnalyzeClasses(packagesOf = MyApplicationRoot.class, importOptions = DoNotIncludeTests.class)\npublic @interface AnalyzeMainClasses {}\n\n\n\nThis annotation can then be used on test classes without repeating the specific configuration of @AnalyzeClasses:\n\n\n\n@AnalyzeMainClasses\npublic class ArchitectureTest {\n // ...\n}\n\n\n\n\n9.1.3. Controlling the Cache\n\nBy default, all classes will be cached by location. This means that between different\ntest class runs imported Java classes will be reused, if the exact combination of locations has already\nbeen imported.\n\n\nIf the heap runs low, and thus the garbage collector has to do a big sweep in one run,\nthis can cause a noticeable delay. On the other hand, if it is known that no other test class will\nreuse the imported Java classes, it would make sense to deactivate this cache.\n\n\nThis can be achieved by configuring CacheMode.PER_CLASS, e.g.\n\n\n\n@AnalyzeClasses(packages = \"com.myapp.special\", cacheMode = CacheMode.PER_CLASS)\n\n\n\nThe Java classes imported during this test run will not be cached by location and just be reused within\nthe same test class. After all tests of this class have been run,\nthe imported Java classes will simply be dropped.\n\n\n\n9.1.4. Ignoring Tests\n\nIt is possible to skip tests by annotating them with @ArchIgnore, for example:\n\n\n\npublic class ArchitectureTest {\n\n // will run\n @ArchTest\n public static final ArchRule rule1 = classes().should()...\n\n // won't run\n @ArchIgnore\n @ArchTest\n public static final ArchRule rule2 = classes().should()...\n}\n\n\n\nNote for users of JUnit 5 & 6: the annotation @Disabled has no effect here.\nInstead, @ArchIgnore should be used.\n\n\n\n9.1.5. Grouping Rules\n\nOften, a project might end up with different categories of rules, for example \"service rules\"\nand \"persistence rules\". It is possible to write one class for each set of rules, and then\nrefer to those sets from another test:\n\n\n\npublic class ServiceRules {\n @ArchTest\n public static final ArchRule ruleOne = ...\n\n // further rules\n}\n\npublic class PersistenceRules {\n @ArchTest\n public static final ArchRule ruleOne = ...\n\n // further rules\n}\n\n// @RunWith(ArchUnitRunner.class) // Enable this line for JUnit 4\n@AnalyzeClasses\npublic class ArchitectureTest {\n\n @ArchTest\n static final ArchTests serviceRules = ArchTests.in(ServiceRules.class);\n\n @ArchTest\n static final ArchTests persistenceRules = ArchTests.in(PersistenceRules.class);\n\n}\n\n\n\nThe runner will include all @ArchTest annotated members within ServiceRules and PersistenceRules and evaluate\nthem against the classes declared within @AnalyzeClasses on ArchitectureTest.\nThis also allows an easy reuse of a rule library in different projects or modules.\n\n\n\n9.1.6. Executing Single Rules\n\nIt is possible to filter specific rules (e.g. @ArchTest fields) via archunit.properties (compare Advanced Configuration).\n\n\narchunit.properties\n\n# Specify the field or method name here. Multiple names can be joined by ','\njunit.testFilter=my_custom_rule_field\n\n\n\nAs always with archunit.properties, this can also be passed dynamically using a system property,\nE.g. passing\n\n\n\n-Darchunit.junit.testFilter=my_custom_rule_field\n\n\n\n\n9.1.7. Generating Display Names\n\nArchUnit offers the possibility to generate more readable names in the test report by replacing underscores in the\noriginal rule names by spaces. For example, if a method or field is named\n\n\n\nsome_Field_or_Method_rule\n\n\n\nthis will appear as\n\n\n\nsome Field or Method rule\n\n\n\nin the test report.\n\n\nThis is similar to JUnit 5’s @DisplayNameGeneration annotation, but because this display name generation does not\nfit well with ArchUnit’s rule execution and because we’d like to offer this feature for JUnit 4 as well, you can enable\ndisplay name generation in ArchUnit with a configuration property (see Advanced Configuration):\n\n\narchunit.properties\n\njunit.displayName.replaceUnderscoresBySpaces=true\n\n\n\nIf you omit the property (or set it to false), the original rule names are used as display names.\n\n\n\n\n\n\n10. Advanced Configuration\n\n\nSome behavior of ArchUnit can be centrally configured by adding a file archunit.properties\nto the root of the classpath (e.g. under src/test/resources).\nThis section will outline some global configuration options.\n\n\n10.1. Overriding configuration\n\nArchUnit will use exactly the archunit.properties file returned by the context\nClassLoader from the classpath root, via the standard Java resource loading mechanism.\n\n\nIt is possible to override any property from archunit.properties, by passing a system property\nto the respective JVM process executing ArchUnit:\n\n\n\n-Darchunit.propertyName=propertyValue\n\n\n\nE.g. to override the property resolveMissingDependenciesFromClassPath described in the next section, it would be possible to pass:\n\n\n\n-Darchunit.resolveMissingDependenciesFromClassPath=false\n\n\n\n\n10.2. Configuring the Resolution Behavior\n\nAs mentioned in Dealing with Missing Classes, it might be preferable to configure a different\nimport behavior if dealing with missing classes wastes too much performance.\nOne way that can be chosen out of the box is to never resolve any missing class from the classpath:\n\n\narchunit.properties\n\nresolveMissingDependenciesFromClassPath=false\n\n\n\nIf you want to resolve just some classes from the classpath (e.g. to import missing classes from\nyour own organization but avoid the performance impact of importing classes from 3rd party packages),\nit is possible to configure only specific packages to be resolved from the classpath:\n\n\narchunit.properties\n\nclassResolver=com.tngtech.archunit.core.importer.resolvers.SelectedClassResolverFromClasspath\nclassResolver.args=some.pkg.one,some.pkg.two\n\n\n\nThis configuration would only resolve the packages some.pkg.one and some.pkg.two from the\nclasspath, and stub all other missing classes.\n\n\nThe last example also demonstrates, how the behavior can be customized freely, for example\nif classes are imported from a different source and are not on the classpath:\n\n\nFirst Supply a custom implementation of\n\n\n\ncom.tngtech.archunit.core.importer.resolvers.ClassResolver\n\n\n\nThen configure it\n\n\narchunit.properties\n\nclassResolver=some.pkg.MyCustomClassResolver\n\n\n\nIf the resolver needs some further arguments, create a public constructor with one List<String>\nargument, and supply the concrete arguments as\n\n\narchunit.properties\n\nclassResolver.args=myArgOne,myArgTwo\n\n\n\nFor further details, compare the sources of SelectedClassResolverFromClasspath.\n\n\n10.2.1. Configuring the Number of Resolution Iterations\n\nIt is also possible to apply a more fine-grained configuration to the import dependency resolution behavior.\n\n\nIn particular, the ArchUnit importer distinguishes the following types of import dependencies:\n\n\n\n\nmember types (e.g. the type of a field of a class, type of a method parameter, etc.)\n\n\naccesses to types (e.g. a method calls another method of a different class)\n\n\nsupertypes (i.e. superclasses that are extended and interfaces that are implemented)\n\n\npermitted subclasses (i.e. classes that are permitted to extend or implement a sealed class or interface)\n\n\nenclosing types (i.e. outer classes of nested classes)\n\n\nannotation types, including parameters of annotations\n\n\ngeneric signature types (i.e. types that comprise a parameterized type, like List and String for List<String>)\n\n\n\n\nFor each of these dependency types it is possible to configure the maximum number of iterations to traverse the dependencies.\nE.g. let us assume we configure the maximum iterations as 2 for member types and we have a class A that declares a field of type B which in turn holds a field of type C.\nOn the first run we would automatically resolve the type B as a dependency of A.\nOn the second run we would then also resolve C as a member type dependency of B.\n\n\nThe configuration parameters with their defaults are the following:\n\n\narchunit.properties\n\nimport.dependencyResolutionProcess.maxIterationsForMemberTypes = 1\nimport.dependencyResolutionProcess.maxIterationsForAccessesToTypes = 1\nimport.dependencyResolutionProcess.maxIterationsForSupertypes = -1\nimport.dependencyResolutionProcess.maxIterationsForPermittedSubclasses = -1\nimport.dependencyResolutionProcess.maxIterationsForEnclosingTypes = -1\nimport.dependencyResolutionProcess.maxIterationsForAnnotationTypes = -1\nimport.dependencyResolutionProcess.maxIterationsForGenericSignatureTypes = -1\n\n\n\nNote that setting a property to a negative value (e.g. -1) will not stop the resolution until all types for the given sort of dependency have been resolved.\nE.g. for generic type signatures a value of -1 will by default lead to all types comprising the signature Map<?, List<? extends String>> (that is Map, List and String) to be automatically resolved.\n\n\nSetting a property to 0 will disable the automatic resolution,\nwhich can lead to types being only partially imported or stubbed.\nFor these types the information is likely not complete or even wrong (e.g. an interface might be reported as class if the bytecode was never analyzed, or annotations might be missing).\n\n\nOn the other hand, bigger (or negative) values can have a massive performance impact.\nThe defaults should provide a reasonable behavior.\nThey include the class graph for all types that are used by members or accesses directly and cut the resolution at that point.\nHowever, relevant information for these types is fully imported, no matter how many iterations it takes (e.g. supertypes or generic signatures).\n\n\n\n\n10.3. MD5 Sums of Classes\n\nSometimes it can be valuable to record the MD5 sums of classes being imported to track\nunexpected behavior. Since this has a performance impact, it is disabled by default,\nbut it can be activated the following way:\n\n\narchunit.properties\n\nenableMd5InClassSources=true\n\n\n\nIf this feature is enabled, the MD5 sum can be queried as\n\n\n\njavaClass.getSource().get().getMd5sum()\n\n\n\n\n10.4. Fail Rules on Empty Should\n\nBy default, ArchUnit will forbid the should-part of rules to be evaluated against an empty set of classes.\nThe reason is that this can lead to rules that by accident do not check any classes at all.\nTake for example\n\n\n\nclasses().that().resideInAPackage(\"com.myapp.old\").should()...\n\n\n\nNow consider somebody renames the package old to newer.\nThe rule will now always evaluate successfully without any reported error.\nHowever, it actually does not check any classes at all anymore.\nThis is likely not what most users want.\nThus, by default ArchUnit will fail checking the rule in this case.\nIf you want to allow evaluating such rules,\ni.e. where the actual input to the should-clause is empty,\nyou can use one of the following ways:\n\n\nAllow Empty Should on a Per-Rule Basis\n\n\nOn each ArchRule you can use the method ArchRule.allowEmptyShould(..) to override the behavior\nfor a single rule, e.g.\n\n\n\n// create a rule that allows that no classes are passed to the should-clause\nclasses().that()...should()...allowEmptyShould(true)\n\n\n\nAllow Empty Should Globally\n\n\nTo allow all rules to be evaluated without checking any classes you can set the following property:\n\n\narchunit.properties\n\narchRule.failOnEmptyShould=false\n\n\n\n\n10.5. Custom Error Messages\n\nYou can configure a custom format to display the failures of a rule.\n\n\nFirst Supply a custom implementation of\n\n\n\ncom.tngtech.archunit.lang.FailureDisplayFormat\n\n\n\nThen configure it\n\n\narchunit.properties\n\nfailureDisplayFormat=some.pkg.MyCustomFailureDisplayFormat\n\n\n\nOne example would be to shorten the fully qualified class names in failure messages:\n\n\n\nprivate static class SimpleClassNameFailureFormat implements FailureDisplayFormat {\n @Override\n public String formatFailure(HasDescription rule, FailureMessages failureMessages, Priority priority) {\n String failureDetails = failureMessages.stream()\n .map(message -> message.replaceAll(\"<(?:\\\\w+\\\\.)+([A-Z][^>]*)>\", \"<$1>\"))\n .collect(joining(lineSeparator()));\n\n return String.format(\"Architecture Violation [Priority: %s] - Rule '%s' was violated (%s):%n%s\",\n priority.asString(), rule.getDescription(), failureMessages.getInformationAboutNumberOfViolations(), failureDetails);\n }\n}\n\n\n\nNote that due to the free format how violation texts can be composed,\nin particular by custom predicates and conditions,\nthere is at the moment no more sophisticated way than plain text parsing.\nUsers can tailor this to their specific environments where they know\nwhich sorts of failure formats can appear in practice.\n\nThe tool call succeeded but the output was truncated. Full output saved to: /home/nick/.local/share/mimocode/tool-output/tool_g001a087defd3c001uIA1Uoded\nUse the actor tool to have explore agent process this file with Grep and Read (with offset/limit). Do NOT read the full file yourself - delegate to save context.","metadata":{"truncated":true,"outputPath":"/home/nick/.local/share/mimocode/tool-output/tool_g001a087defd3c001uIA1Uoded"},"title":"https://www.archunit.org/userguide/html/000_Index.html (text/html; charset=utf-8)","time":{"start":1788985932788,"end":1788985933118}},"id":"prt_g001a087defa300019hoeGKIf5","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo"}} +{"type":"tool_use","timestamp":1788985934027,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_d663dbd68edd4446b84a72a5","state":{"status":"completed","input":{"format":"text","url":"https://raw.githubusercontent.com/jendrikseipp/vulture/main/README.md"},"output":"# Vulture - Find dead code\n\n[![PyPI Version](https://img.shields.io/pypi/v/vulture.svg)](https://pypi.python.org/pypi/vulture)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/vulture.svg)](https://anaconda.org/conda-forge/vulture)\n![CI:Test](https://github.com/jendrikseipp/vulture/workflows/CI/badge.svg)\n[![Codecov Badge](https://codecov.io/gh/jendrikseipp/vulture/branch/main/graphs/badge.svg)](https://codecov.io/gh/jendrikseipp/vulture?branch=main)\n\nVulture finds unused code in Python programs. This is useful for\ncleaning up and finding errors in large code bases. If you run Vulture\non both your library and test suite you can find untested code.\n\nDue to Python's dynamic nature, static code analyzers like Vulture are\nlikely to miss some dead code. Also, code that is only called implicitly\nmay be reported as unused. Nonetheless, Vulture can be a very helpful\ntool for higher code quality.\n\n## Features\n\n* fast: uses static code analysis\n* tested: tests itself and has complete test coverage\n* complements pyflakes and has the same output syntax\n* sorts unused classes and functions by size with `--sort-by-size`\n\n## Installation\n\n $ pip install vulture\n\n## Usage\n\n $ vulture myscript.py # or\n $ python3 -m vulture myscript.py\n $ vulture myscript.py mypackage/\n $ vulture myscript.py --min-confidence 100 # Only report 100% dead code.\n\nThe provided arguments may be Python files or directories. For each\ndirectory Vulture analyzes all contained\n\\*.py files.\n\nAfter you have found and deleted dead code, run Vulture again, because\nit may discover more dead code.\n\n## Types of unused code\n\nIn addition to finding unused functions, classes, etc., Vulture can detect\nunreachable code. Each chunk of dead code is assigned a *confidence value*\nbetween 60% and 100%, where a value of 100% signals that it is certain that the\ncode won't be executed. Values below 100% are *very rough* estimates (based on\nthe type of code chunk) for how likely it is that the code is unused.\n\n| Code type | Confidence value |\n|--------------------------------------------------------|------------------|\n| function/method/class argument, unreachable code | 100% |\n| import | 90% |\n| attribute, class, function, method, property, variable | 60% |\n\nYou can use the `--min-confidence` flag to set the minimum confidence\nfor code to be reported as unused. Use `--min-confidence 100` to only\nreport code that is guaranteed to be unused within the analyzed files.\n\n## Handling false positives\n\nWhen Vulture incorrectly reports chunks of code as unused, you have\nseveral options for suppressing the false positives. If fixing your false\npositives could benefit other users as well, please file an issue report.\n\n#### Whitelists\n\nThe recommended option is to add used code that is reported as unused to a\nPython module and add it to the list of scanned paths. To obtain such a\nwhitelist automatically, pass `--make-whitelist` to Vulture:\n\n $ vulture mydir --make-whitelist > whitelist.py\n $ vulture mydir whitelist.py\n\nNote that the resulting `whitelist.py` file will contain valid Python\nsyntax, but for Python to be able to *run* it, you will usually have to\nmake some modifications.\n\nWe collect whitelists for common Python modules and packages in\n`vulture/whitelists/` (pull requests are welcome).\n\n#### Ignoring files\n\nIf you want to ignore a whole file or directory, use the `--exclude` parameter\n(e.g., `--exclude \"*settings.py,*/docs/*.py,*/test_*.py,*/.venv/*.py\"`). The\nexclude patterns are matched against absolute paths.\n\n#### Flake8 noqa comments\n\n\n\nFor compatibility with [flake8](https://flake8.pycqa.org/), Vulture\nsupports the [F401 and\nF841](https://flake8.pycqa.org/en/latest/user/error-codes.html) error\ncodes for ignoring unused imports (`# noqa: F401`) and unused local\nvariables (`# noqa: F841`). However, we recommend using whitelists instead\nof `noqa` comments, since `noqa` comments add visual noise to the code and\nmake it harder to read.\n\n#### Ignoring names\n\nYou can use `--ignore-names foo*,ba[rz]` to let Vulture ignore all names\nstarting with `foo` and the names `bar` and `baz`. Additionally, the\n`--ignore-decorators` option can be used to ignore the names of functions\ndecorated with the given decorator (but not their arguments or function body).\nThis is helpful for example in Flask\nprojects, where you can use `--ignore-decorators \"@app.route\"` to ignore all\nfunction names with the `@app.route` decorator. Note that Vulture simplifies\ndecorators it cannot parse: `@foo.bar(x, y)` becomes \"@foo.bar\" and\n`@foo.bar(x, y).baz` becomes \"@\" internally.\n\nWe recommend using whitelists instead of `--ignore-names` or\n`--ignore-decorators` whenever possible, since whitelists are\nautomatically checked for syntactic correctness when passed to Vulture\nand often you can even pass them to your Python interpreter and let it\ncheck that all whitelisted code actually still exists in your project.\n\n#### Marking unused variables\n\nThere are situations where you can't just remove unused variables, e.g.,\nin function signatures. The recommended solution is to use the `del`\nkeyword as described in the\n[PyLint manual](http://pylint-messages.wikidot.com/messages:w0613) and on\n[StackOverflow](https://stackoverflow.com/a/14836005):\n\n```python\ndef foo(x, y):\n del y\n return x + 3\n```\n\nVulture will also ignore all variables that start with an underscore, so\nyou can use `_x, y = get_pos()` to mark unused tuple assignments or\nfunction arguments, e.g., `def foo(x, _y)`.\n\n#### Minimum confidence\n\nRaise the minimum [confidence value](#types-of-unused-code) with the `--min-confidence` flag.\n\n#### Unreachable code\n\nIf Vulture complains about code like `if False:`, you can use a Boolean\nflag `debug = False` and write `if debug:` instead. This makes the code\nmore readable and silences Vulture.\n\n#### Forward references for type annotations\n\nSee [#216](https://github.com/jendrikseipp/vulture/issues/216). For\nexample, instead of `def foo(arg: \"Sequence\"): ...`, we recommend using\n\n``` python\nfrom __future__ import annotations\n\ndef foo(arg: Sequence):\n ...\n```\n\n\n## Configuration\n\nYou can also store command line arguments in `pyproject.toml` under the\n`tool.vulture` section. Simply remove leading dashes and replace all\nremaining dashes with underscores.\n\nOptions given on the command line have precedence over options in\n`pyproject.toml`.\n\nExample Config:\n\n``` toml\n[tool.vulture]\nexclude = [\"*file*.py\", \"dir/\"]\nignore_decorators = [\"@app.route\", \"@require_*\"]\nignore_names = [\"visit_*\", \"do_*\"]\nmake_whitelist = true\nmin_confidence = 80\npaths = [\"myscript.py\", \"mydir\", \"whitelist.py\"]\nsort_by_size = true\nverbose = true\n```\n\nVulture will automatically look for a `pyproject.toml` in the current working directory.\n\nTo use a `pyproject.toml` in another directory, you can use the `--config path/to/pyproject.toml` flag.\n\n## Integrations\n\nYou can use a [pre-commit](https://pre-commit.com/#install) hook to run\nVulture before each commit. For this, install pre-commit and add the\nfollowing to the `.pre-commit-config.yaml` file in your repository:\n\n```yaml\nrepos:\n - repo: https://github.com/jendrikseipp/vulture\n rev: 'v2.3' # or any later Vulture version\n hooks:\n - id: vulture\n```\n\nThen run `pre-commit install`. Finally, create a `pyproject.toml` file\nin your repository and specify all files that Vulture should check under\n`[tool.vulture] --> paths` (see above).\n\nThere's also a [GitHub Action for Vulture](https://github.com/gtkacz/vulture-action),\na [VS Code extension](https://marketplace.visualstudio.com/items?itemName=sebastienfi.dead-code-finder)\nand you can use Vulture programmatically. For example:\n\n``` python\nimport vulture\n\nv = vulture.Vulture()\nv.scavenge(['.'])\nunused_code = v.get_unused_code() # returns a list of `Item` objects\n```\n\n## How does it work?\n\nVulture uses the `ast` module to build abstract syntax trees for all\ngiven files. While traversing all syntax trees it records the names of\ndefined and used objects. Afterwards, it reports the objects which have\nbeen defined, but not used. This analysis ignores scopes and only takes\nobject names into account.\n\nVulture also detects unreachable code by looking for code after\n`return`, `break`, `continue` and `raise` statements, and by searching\nfor unsatisfiable `if`- and `while`-conditions.\n\n## Sort by size\n\nWhen using the `--sort-by-size` option, Vulture sorts unused code by its\nnumber of lines. This helps developers prioritize where to look for dead\ncode first.\n\n## Examples\n\nConsider the following Python script (`dead_code.py`):\n\n``` python\nimport os\n\nclass Greeter:\n def greet(self):\n print(\"Hi\")\n\ndef hello_world():\n message = \"Hello, world!\"\n greeter = Greeter()\n func_name = \"greet\"\n greet_func = getattr(greeter, func_name)\n greet_func()\n\nif __name__ == \"__main__\":\n hello_world()\n```\n\nCalling :\n\n $ vulture dead_code.py\n\nresults in the following output:\n\n dead_code.py:1: unused import 'os' (90% confidence)\n dead_code.py:4: unused function 'greet' (60% confidence)\n dead_code.py:8: unused variable 'message' (60% confidence)\n\nVulture correctly reports `os` and `message` as unused but it fails to\ndetect that `greet` is actually used. The recommended method to deal\nwith false positives like this is to create a whitelist Python file.\n\n**Preparing whitelists**\n\nIn a whitelist we simulate the usage of variables, attributes, etc. For\nthe program above, a whitelist could look as follows:\n\n``` python\n# whitelist_dead_code.py\nfrom dead_code import Greeter\nGreeter.greet\n```\n\nAlternatively, you can pass `--make-whitelist` to Vulture and obtain an\nautomatically generated whitelist.\n\nPassing both the original program and the whitelist to Vulture\n\n $ vulture dead_code.py whitelist_dead_code.py\n\nmakes Vulture ignore the `greet` method:\n\n dead_code.py:1: unused import 'os' (90% confidence)\n dead_code.py:8: unused variable 'message' (60% confidence)\n\n\n\n## Exit codes\n\n| Exit code | Description |\n|-----------|------------------------------------------------------------|\n| 0 | No dead code found |\n| 1 | Invalid input (file missing, syntax error, wrong encoding) |\n| 2 | Invalid command line arguments |\n| 3 | Dead code found |\n\n## Similar programs\n\n - [pyflakes](https://pypi.org/project/pyflakes/) finds unused imports\n and unused local variables (in addition to many other programmatic\n errors).\n - [coverage](https://pypi.org/project/coverage/) finds unused code\n more reliably than Vulture, but requires all branches of the code to\n actually be run.\n - [uncalled](https://pypi.org/project/uncalled/) finds dead code by\n using the abstract syntax tree (like Vulture), regular expressions,\n or both.\n - [dead](https://pypi.org/project/dead/) finds dead code by using the\n abstract syntax tree (like Vulture).\n\n## Participate\n\nPlease visit to report any\nissues or to make pull requests.\n\n - Contributing guide:\n [CONTRIBUTING.md](https://github.com/jendrikseipp/vulture/blob/main/CONTRIBUTING.md)\n - Release notes:\n [CHANGELOG.md](https://github.com/jendrikseipp/vulture/blob/main/CHANGELOG.md)\n - Roadmap:\n [TODO.md](https://github.com/jendrikseipp/vulture/blob/main/TODO.md)\n","metadata":{"truncated":false},"title":"https://raw.githubusercontent.com/jendrikseipp/vulture/main/README.md (text/plain; charset=utf-8)","time":{"start":1788985932786,"end":1788985934024}},"id":"prt_g001a087def957001PnGpRG0oD","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo"}} +{"type":"step_finish","timestamp":1788985934055,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df00ca00152GcOdEfj","reason":"tool-calls","snapshot":"bee0139b777d7b04ee260de01d7c96c6612fc4a8","messageID":"msg_g001a087deb9a9001ZJ2qt2iSo","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":64742,"input":3469,"output":601,"reasoning":0,"cache":{"write":0,"read":60672}},"cost":0}} +{"type":"step_start","timestamp":1788985941317,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df1d430017kXqEJjNh","messageID":"msg_g001a087df010e001x90E3F2vZ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"cb75a4be9e3748b7a8bd79a31b4c4efa3db1628b","type":"step-start"}} +{"type":"text","timestamp":1788985941498,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df1db4002by0LMcHhz","messageID":"msg_g001a087df010e001x90E3F2vZ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Import-linter README.rst 404'd — retrying with README.md:","time":{"start":1788985941428,"end":1788985941496}}} +{"type":"tool_use","timestamp":1788985941673,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"webfetch","callID":"call_211a6c02d25b4846a959e667","state":{"status":"completed","input":{"format":"text","url":"https://raw.githubusercontent.com/seddonym/import-linter/main/README.md"},"output":"
\n\n\"Import\n\n**Lint your Python architecture.**\n\n[![Package version](https://img.shields.io/pypi/v/import-linter.svg)](https://pypi.org/project/import-linter)\n[![Python versions](https://img.shields.io/pypi/pyversions/import-linter.svg)](https://pypi.org/project/import-linter)\n[![CI status](https://github.com/seddonym/import-linter/actions/workflows/main.yml/badge.svg)](https://github.com/seddonym/import-linter/actions/workflows/main.yml)\n[![BSD license](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)\n\n
\n\n\n**Import Linter** allows you to impose constraints on the imports between your Python modules.\n\nIt also provides a browser-based user interface for exploring the architecture of any Python package.\n\n

\n Get started.\n

","metadata":{"truncated":false},"title":"https://raw.githubusercontent.com/seddonym/import-linter/main/README.md (text/plain; charset=utf-8)","time":{"start":1788985941498,"end":1788985941672}},"id":"prt_g001a087df1db60017fY0mx3ng","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087df010e001x90E3F2vZ"}} +{"type":"step_finish","timestamp":1788985941698,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df1ea9001GJXhKwFuh","reason":"tool-calls","snapshot":"8c0d09b6554e33f3e7f67cb13456a6638f742041","messageID":"msg_g001a087df010e001x90E3F2vZ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":80873,"input":16599,"output":82,"reasoning":0,"cache":{"write":0,"read":64192}},"cost":0}} +{"type":"step_start","timestamp":1788985969361,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df8ace001yhOg3nX4S","messageID":"msg_g001a087df1ee8001pH6mb71qo","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"db80d14f28503aa9eec3411985189d545d807145","type":"step-start"}} +{"type":"text","timestamp":1788985970742,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df901b002UhIqRtyMN","messageID":"msg_g001a087df1ee8001pH6mb71qo","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"I now have enough evidence (5 sources read, 6 successful searches recorded). Before writing the artifact, let me check how issue-310 recorded OUTWARD in its task_tree (to match finalization conventions).","time":{"start":1788985970715,"end":1788985970739}}} +{"type":"tool_use","timestamp":1788985970766,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_4c82bf53e5204beb874d802a","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-310/task_tree.json'))\nprint('current_stage:',d.get('current_stage'))\nprint('stage_history:',json.dumps(d.get('stage_history'),indent=1)[:800])\nprint('artifacts:',json.dumps(d.get('artifacts'))[:400])\n\"","description":"Check issue-310 task_tree finalization pattern"},"output":"current_stage: OUTWARD\nstage_history: [\n {\n \"stage\": \"ISSUE\",\n \"round\": 1,\n \"completed_at\": \"auto\",\n \"artifact_keys\": [\n \"contract\",\n \"deterministic\"\n ]\n },\n {\n \"stage\": \"INWARD\",\n \"round\": 1,\n \"completed_at\": \"auto\",\n \"artifact_keys\": [\n \"context\",\n \"deterministic\",\n \"claims\"\n ]\n },\n {\n \"stage\": \"OUTWARD\",\n \"round\": 1,\n \"failed\": true,\n \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"\n },\n {\n \"stage\": \"OUTWARD\",\n \"round\": 1,\n \"failed\": true,\n \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"\n }\n]\nartifacts: {\"ISSUE\": {\"contract\": {\"issue\": 310, \"title\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\", \"question_type\": \"repo-specific\", \"sub_questions\": [\"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\"], \"required_stages\": [\"ISSUE\", \"INWARD\", \"SYNTHESIS\", \"QUALIF\n","metadata":{"output":"current_stage: OUTWARD\nstage_history: [\n {\n \"stage\": \"ISSUE\",\n \"round\": 1,\n \"completed_at\": \"auto\",\n \"artifact_keys\": [\n \"contract\",\n \"deterministic\"\n ]\n },\n {\n \"stage\": \"INWARD\",\n \"round\": 1,\n \"completed_at\": \"auto\",\n \"artifact_keys\": [\n \"context\",\n \"deterministic\",\n \"claims\"\n ]\n },\n {\n \"stage\": \"OUTWARD\",\n \"round\": 1,\n \"failed\": true,\n \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"\n },\n {\n \"stage\": \"OUTWARD\",\n \"round\": 1,\n \"failed\": true,\n \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"\n }\n]\nartifacts: {\"ISSUE\": {\"contract\": {\"issue\": 310, \"title\": \"ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\", \"question_type\": \"repo-specific\", \"sub_questions\": [\"Address: ADAPT-1 B1 (LOCAL-LIKE): telemetry markers never invoked in production path \\u2014 model_time/tool_time always 0\"], \"required_stages\": [\"ISSUE\", \"INWARD\", \"SYNTHESIS\", \"QUALIF\n","exit":0,"description":"Check issue-310 task_tree finalization pattern","truncated":false},"title":"Check issue-310 task_tree finalization pattern","time":{"start":1788985970759,"end":1788985970763}},"id":"prt_g001a087df90270013yXhojr2R","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087df1ee8001pH6mb71qo"}} +{"type":"step_finish","timestamp":1788985970797,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087df904d001XxTYZ9bMt","reason":"tool-calls","snapshot":"341f2a4db1b62f85375ec37312314410d1556736","messageID":"msg_g001a087df1ee8001pH6mb71qo","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":82188,"input":258,"output":1098,"reasoning":0,"cache":{"write":0,"read":80832}},"cost":0}} +{"type":"step_start","timestamp":1788985977320,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dfa9e6001U4v3CpYqi","messageID":"msg_g001a087df90a1001OEVio9n7o","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"f0e73a323f38f3e746871c7c89855124fb0ea357","type":"step-start"}} +{"type":"tool_use","timestamp":1788985978693,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_2406902b29044539b76cddf8","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-310/task_tree.json'))\nsh=d['stage_history']\nfor e in sh:\n if e.get('stage')=='OUTWARD' and not e.get('failed'):\n print(json.dumps(e,indent=1))\n\"; ls /mnt/workdrive/issue161-harness/research/issue-310/","description":"Find accepted OUTWARD history entry format"},"output":"outward.json\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n","metadata":{"output":"outward.json\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n","exit":0,"description":"Find accepted OUTWARD history entry format","truncated":false},"title":"Find accepted OUTWARD history entry format","time":{"start":1788985978690,"end":1788985978692}},"id":"prt_g001a087dfa9e700198O3HimUh","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087df90a1001OEVio9n7o"}} +{"type":"step_finish","timestamp":1788985978720,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dfaf45001N32aSN0ly","reason":"tool-calls","snapshot":"ff18af15627d153075bf832230d674de8ea92c83","messageID":"msg_g001a087df90a1001OEVio9n7o","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":81686,"input":488,"output":110,"reasoning":0,"cache":{"write":0,"read":81088}},"cost":0}} +{"type":"step_start","timestamp":1788985984581,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dfc643001vaKQN1Ge5","messageID":"msg_g001a087dfaf89001mamxX4USw","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"86ca24c91bfaef9f805752e769000555a68cc3e3","type":"step-start"}} +{"type":"tool_use","timestamp":1788985985924,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_72b5b11016d5444eac2ee814","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-310/task_tree.json'))\nprint(json.dumps(d['stage_history'][-1],indent=1))\nprint('STATUS:',d.get('status'))\nprint('TOP KEYS:',list(d.keys()))\n\"","description":"See last history entry and task_tree keys"},"output":"{\n \"stage\": \"OUTWARD\",\n \"round\": 1,\n \"failed\": true,\n \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"\n}\nSTATUS: ACTIVE\nTOP KEYS: ['issue', 'round', 'current_stage', 'completed_stages', 'artifacts', 'blocking_unknowns', 'status', 'stage_history']\n","metadata":{"output":"{\n \"stage\": \"OUTWARD\",\n \"round\": 1,\n \"failed\": true,\n \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"\n}\nSTATUS: ACTIVE\nTOP KEYS: ['issue', 'round', 'current_stage', 'completed_stages', 'artifacts', 'blocking_unknowns', 'status', 'stage_history']\n","exit":0,"description":"See last history entry and task_tree keys","truncated":false},"title":"See last history entry and task_tree keys","time":{"start":1788985985918,"end":1788985985922}},"id":"prt_g001a087dfc644001at12mbejE","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dfaf89001mamxX4USw"}} +{"type":"step_finish","timestamp":1788985985955,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087dfcb84001CnGbdhMTR","reason":"tool-calls","snapshot":"7f4ff18a96783a91ed55164e0f5d1cf8378e2e63","messageID":"msg_g001a087dfaf89001mamxX4USw","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":81804,"input":115,"output":89,"reasoning":0,"cache":{"write":0,"read":81600}},"cost":0}} +{"type":"step_start","timestamp":1788986009813,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e028d30012igl28P6C","messageID":"msg_g001a087dfcbcb001RLPTuchmV","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"ebcef45ca4426fa432e6838b701be0031905acfd","type":"step-start"}} +{"type":"text","timestamp":1788986010971,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e02c3a001Ln3BEC2Tq","messageID":"msg_g001a087dfcbcb001RLPTuchmV","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Let me check whether accepted JSON artifacts in prior rounds end with the `StageCompleted:` marker:","time":{"start":1788986010682,"end":1788986010968}}} +{"type":"tool_use","timestamp":1788986010983,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_6a00fcf934d44e67a1db6962","state":{"status":"completed","input":{"command":"for f in /mnt/workdrive/research-stage/research/issue-263/outward.json /mnt/workdrive/research-stage/research/issue-262/outward_reading.json /mnt/workdrive/issue161-harness/research/issue-163/outward.json; do echo \"== $f\"; tail -c 120 \"$f\" 2>/dev/null; echo; done","description":"Check tails of prior outward artifacts for marker"},"output":"== /mnt/workdrive/research-stage/research/issue-263/outward.json\nWAL concurrency semantics, Python binding behavior, and PRAGMA tuning; blog O4 supplies a Python worked example.\"\n }\n}\n\n== /mnt/workdrive/research-stage/research/issue-262/outward_reading.json\n test at all (xdist auto-disable), (d) S9's 10x claim should be killed or corroborated by measurement in INNOVATION.\"\n}\n\n== /mnt/workdrive/issue161-harness/research/issue-163/outward.json\n O25-O27, O30-O37, O39, O40, O42-O45) recorded but not read. No synthesis, no design, no code changes performed.\"\n }\n}\n\n","metadata":{"output":"== /mnt/workdrive/research-stage/research/issue-263/outward.json\nWAL concurrency semantics, Python binding behavior, and PRAGMA tuning; blog O4 supplies a Python worked example.\"\n }\n}\n\n== /mnt/workdrive/research-stage/research/issue-262/outward_reading.json\n test at all (xdist auto-disable), (d) S9's 10x claim should be killed or corroborated by measurement in INNOVATION.\"\n}\n\n== /mnt/workdrive/issue161-harness/research/issue-163/outward.json\n O25-O27, O30-O37, O39, O40, O42-O45) recorded but not read. No synthesis, no design, no code changes performed.\"\n }\n}\n\n","exit":0,"description":"Check tails of prior outward artifacts for marker","truncated":false},"title":"Check tails of prior outward artifacts for marker","time":{"start":1788986010980,"end":1788986010982}},"id":"prt_g001a087e02c3e001Meq1F0pVX","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087dfcbcb001RLPTuchmV"}} +{"type":"step_finish","timestamp":1788986011008,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e02d67001dONS6GDL1","reason":"tool-calls","snapshot":"40c0b485b0a30ee26a6c849a434e01cf3c4115ba","messageID":"msg_g001a087dfcbcb001RLPTuchmV","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":82634,"input":175,"output":731,"reasoning":0,"cache":{"write":0,"read":81728}},"cost":0}} +{"type":"step_start","timestamp":1788986017246,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e045dc0019dSswNBnu","messageID":"msg_g001a087e02db2001RZkwSSol6","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"47a5373ece6df8cf985b6e4d21cb496ef18e9bcc","type":"step-start"}} +{"type":"tool_use","timestamp":1788986018971,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_508353879f3a4f9f945b7149","state":{"status":"completed","input":{"command":"grep -l \"StageCompleted\" /mnt/workdrive/research-stage/research/issue-263/* /mnt/workdrive/research-stage/research/issue-262/* /mnt/workdrive/issue161-harness/research/issue-163/* /mnt/workdrive/issue161-harness/research/issue-310/* 2>/dev/null; ls /mnt/workdrive/issue161-harness/research/issue-163/ /mnt/workdrive/issue161-harness/research/issue-310/","description":"Search for StageCompleted marker in prior artifacts"},"output":"/mnt/workdrive/research-stage/research/issue-262/task_tree.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/:\nadversarial.json\napplicability.json\ninnovation.json\noutward.json\nqualify.json\nstage_ADVERSARIAL.events.jsonl\nstage_ADVERSARIAL.mimo.err.log\nstage_APPLICABILITY.events.jsonl\nstage_APPLICABILITY.mimo.err.log\nstage_INNOVATION.events.jsonl\nstage_INNOVATION.mimo.err.log\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\nstage_QUALIFY.events.jsonl\nstage_QUALIFY.mimo.err.log\nstage_SYNTHESIS.events.jsonl\nstage_SYNTHESIS.mimo.err.log\nsynthesis.json\ntask_tree.json\n\n/mnt/workdrive/issue161-harness/research/issue-310/:\noutward.json\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n","metadata":{"output":"/mnt/workdrive/research-stage/research/issue-262/task_tree.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/:\nadversarial.json\napplicability.json\ninnovation.json\noutward.json\nqualify.json\nstage_ADVERSARIAL.events.jsonl\nstage_ADVERSARIAL.mimo.err.log\nstage_APPLICABILITY.events.jsonl\nstage_APPLICABILITY.mimo.err.log\nstage_INNOVATION.events.jsonl\nstage_INNOVATION.mimo.err.log\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\nstage_QUALIFY.events.jsonl\nstage_QUALIFY.mimo.err.log\nstage_SYNTHESIS.events.jsonl\nstage_SYNTHESIS.mimo.err.log\nsynthesis.json\ntask_tree.json\n\n/mnt/workdrive/issue161-harness/research/issue-310/:\noutward.json\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\ntelemetry\n","exit":0,"description":"Search for StageCompleted marker in prior artifacts","truncated":false},"title":"Search for StageCompleted marker in prior artifacts","time":{"start":1788986018967,"end":1788986018969}},"id":"prt_g001a087e045dd002jL0GmmVtH","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e02db2001RZkwSSol6"}} +{"type":"step_finish","timestamp":1788986018995,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e04c9a001L89prAt1I","reason":"tool-calls","snapshot":"0fd98eadeb427d4596f1348a5d0dc5956b145211","messageID":"msg_g001a087e02db2001RZkwSSol6","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":82319,"input":268,"output":131,"reasoning":0,"cache":{"write":0,"read":81920}},"cost":0}} +{"type":"step_start","timestamp":1788986024847,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e0638e001WZ76AeJ6T","messageID":"msg_g001a087e04cde001VNb7U265O","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"c3727c42bad775ff27c044fd389c57f4966755dd","type":"step-start"}} +{"type":"tool_use","timestamp":1788986026992,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_51ad3e5be58f429e99ef2001","state":{"status":"completed","input":{"command":"grep -o \"StageCompleted[^\\\"]*\" /mnt/workdrive/issue161-harness/research/issue-163/task_tree.json 2>/dev/null | head -2; python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\nh=d['stage_history']\nfor e in h:\n if e.get('stage')=='OUTWARD': print(json.dumps(e))\n\" | tail -2","description":"Check issue-163 task_tree OUTWARD acceptance format"},"output":"{\"stage\": \"OUTWARD\", \"round\": 1, \"failed\": true, \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"}\n{\"stage\": \"OUTWARD\", \"round\": 1, \"completed_at\": \"auto\", \"artifact_keys\": [\"stage\", \"issue\", \"round\", \"attempt\", \"discovery\", \"reading\", \"contradictory_evidence\", \"limitations\", \"supporting_evidence\", \"alternatives\", \"adjacent_field_leads\", \"unresolved_questions\", \"tool_receipts\", \"worker_artifact_file\", \"event_counts\", \"elapsed\"]}\n","metadata":{"output":"{\"stage\": \"OUTWARD\", \"round\": 1, \"failed\": true, \"reason\": \"OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \\u2014 search snippets alone do not count as evidence\"}\n{\"stage\": \"OUTWARD\", \"round\": 1, \"completed_at\": \"auto\", \"artifact_keys\": [\"stage\", \"issue\", \"round\", \"attempt\", \"discovery\", \"reading\", \"contradictory_evidence\", \"limitations\", \"supporting_evidence\", \"alternatives\", \"adjacent_field_leads\", \"unresolved_questions\", \"tool_receipts\", \"worker_artifact_file\", \"event_counts\", \"elapsed\"]}\n","exit":0,"description":"Check issue-163 task_tree OUTWARD acceptance format","truncated":false},"title":"Check issue-163 task_tree OUTWARD acceptance format","time":{"start":1788986026988,"end":1788986026990}},"id":"prt_g001a087e0638f0017umTCSMuk","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e04cde001VNb7U265O"}} +{"type":"step_finish","timestamp":1788986027016,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e06bef0020XZReX0CC","reason":"tool-calls","snapshot":"ec8754f2a7b59a69b5d84b80a5e79fd87cd28b41","messageID":"msg_g001a087e04cde001VNb7U265O","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":82681,"input":315,"output":126,"reasoning":0,"cache":{"write":0,"read":82240}},"cost":0}} +{"type":"step_start","timestamp":1788986072391,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e11d44001IYJInrxM8","messageID":"msg_g001a087e06c39001t49NV4AO2","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"f3a6dd3cf7f5f8ebcb0f4e75dcabe1b41fe614fb","type":"step-start"}} +{"type":"text","timestamp":1788986075026,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e1262c001krrX1Potc","messageID":"msg_g001a087e06c39001t49NV4AO2","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Now let me verify the repo-side sources referenced by issue #311 (receipts), then write the artifact.","time":{"start":1788986074668,"end":1788986075021}}} +{"type":"tool_use","timestamp":1788986075040,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_0539798c2feb417f8bc13d91","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/rune/src/rune/orchestration/benchmark.py /mnt/workdrive/rune/scripts/ 2>/dev/null | head; grep -n \"def \\|report\" /mnt/workdrive/rune/src/rune/orchestration/benchmark.py 2>/dev/null | head -15","description":"Verify benchmark.py reporting pattern"},"output":"/mnt/workdrive/rune/src/rune/orchestration/benchmark.py\n\n/mnt/workdrive/rune/scripts/:\nauto_bump_pr.py\ncapture_failure.py\ncarry_forward_check.py\ncheck_dep_lock.py\ncheck_discharge_contract.py\ncheck_version_surfaces.py\ncreate_release.py\n22:def fingerprint() -> dict:\n40:def run_batches(\n51:def p95(values: list[float]) -> float:\n57:def evaluate_p95_regression(\n","metadata":{"output":"/mnt/workdrive/rune/src/rune/orchestration/benchmark.py\n\n/mnt/workdrive/rune/scripts/:\nauto_bump_pr.py\ncapture_failure.py\ncarry_forward_check.py\ncheck_dep_lock.py\ncheck_discharge_contract.py\ncheck_version_surfaces.py\ncreate_release.py\n22:def fingerprint() -> dict:\n40:def run_batches(\n51:def p95(values: list[float]) -> float:\n57:def evaluate_p95_regression(\n","exit":0,"description":"Verify benchmark.py reporting pattern","truncated":false},"title":"Verify benchmark.py reporting pattern","time":{"start":1788986075038,"end":1788986075039}},"id":"prt_g001a087e12645001y68audWjn","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e06c39001t49NV4AO2"}} +{"type":"tool_use","timestamp":1788986075049,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_481e9a5d7fe34f4eb0a09bb3","state":{"status":"completed","input":{"command":"ls -la /mnt/workdrive/rune/config/v1_authority_map.json 2>/dev/null && python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/rune/config/v1_authority_map.json'))\nprint(type(d).__name__, len(d) if hasattr(d,'__len__') else '')\nprint(str(d)[:300])\n\"","description":"Verify v1_authority_map.json exists and shape"},"output":"-rw-r--r-- 1 nick nick 26538 Sep 8 04:50 /mnt/workdrive/rune/config/v1_authority_map.json\ndict 9\n{'schema_version': 1, 'status': 'decision-record-not-migration', 'epic': '#16', 'issue': '#17', 'frozen_reference': {'tag': 'v0.42.0', 'commit': '98b8a7048455dd1a5c6fe219c114502549b8227c', 'recoverable': True}, 'v1_branch': 'v1/p0-01-authority-map', 'invariant': 'No subsystem may inject model contex\n","metadata":{"output":"-rw-r--r-- 1 nick nick 26538 Sep 8 04:50 /mnt/workdrive/rune/config/v1_authority_map.json\ndict 9\n{'schema_version': 1, 'status': 'decision-record-not-migration', 'epic': '#16', 'issue': '#17', 'frozen_reference': {'tag': 'v0.42.0', 'commit': '98b8a7048455dd1a5c6fe219c114502549b8227c', 'recoverable': True}, 'v1_branch': 'v1/p0-01-authority-map', 'invariant': 'No subsystem may inject model contex\n","exit":0,"description":"Verify v1_authority_map.json exists and shape","truncated":false},"title":"Verify v1_authority_map.json exists and shape","time":{"start":1788986075044,"end":1788986075047}},"id":"prt_g001a087e12631001RhmHJ8Hcq","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e06c39001t49NV4AO2"}} +{"type":"step_finish","timestamp":1788986075076,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e127a9001Y6dCBdlVn","reason":"tool-calls","snapshot":"a900ff522f3cefcc2e58515ce3b5553254ee4df2","messageID":"msg_g001a087e06c39001t49NV4AO2","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":84757,"input":272,"output":1925,"reasoning":0,"cache":{"write":0,"read":82560}},"cost":0}} +{"type":"step_start","timestamp":1788986078184,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e133e7001IWnPpHj9I","messageID":"msg_g001a087e128020016cCMeAvJ1","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"fbc093115a2c22589787a016dc2373dc84f5a767","type":"step-start"}} +{"type":"tool_use","timestamp":1788986079601,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_9c63bea8c5fa41489f3f9a0f","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/rune/scripts/ | grep -iE \"walkman|report|wiring|audit|truth\"; echo SEP; python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/rune/config/v1_authority_map.json'))\nprint(list(d.keys()))\n\"","description":"Check walkman/report scripts and authority map keys"},"output":"SEP\n['schema_version', 'status', 'epic', 'issue', 'frozen_reference', 'v1_branch', 'invariant', 'dispositions', 'responsibilities']\n","metadata":{"output":"SEP\n['schema_version', 'status', 'epic', 'issue', 'frozen_reference', 'v1_branch', 'invariant', 'dispositions', 'responsibilities']\n","exit":0,"description":"Check walkman/report scripts and authority map keys","truncated":false},"title":"Check walkman/report scripts and authority map keys","time":{"start":1788986079598,"end":1788986079600}},"id":"prt_g001a087e133e8001jRQvL262y","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e128020016cCMeAvJ1"}} +{"type":"step_finish","timestamp":1788986079627,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e13971001CIkNGXT1R","reason":"tool-calls","snapshot":"49d07ccb922443a85fd31c3130370fc91af16211","messageID":"msg_g001a087e128020016cCMeAvJ1","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":83373,"input":407,"output":86,"reasoning":0,"cache":{"write":0,"read":82880}},"cost":0}} +{"type":"step_start","timestamp":1788986082538,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e144df001uFHXG8bd1","messageID":"msg_g001a087e139b6001KbOX4xQu9","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"dcbad889170d56a721341d5fb9afbbe9c913e946","type":"step-start"}} +{"type":"tool_use","timestamp":1788986083743,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_753fe369cc0f4b348fffd20d","state":{"status":"completed","input":{"command":"grep -rn \"walkman\" /mnt/workdrive/rune/src/rune/*.py /mnt/workdrive/rune/scripts/*.py 2>/dev/null | head -5; 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grep -n \"def \\|subsystem\\|report\" /mnt/workdrive/rune/scripts/gen_prd_state.py | head -12","description":"Search walkman and gen_prd_state report pattern"},"output":"29:def version() -> str:\n35:def git_head() -> str:\n44:def pipeline_state() -> str:\n100:def tui_state() -> str:\n115: \"### TUI-adjacent subsystems (impact map)\",\n129:def regenerate(path: Path, new_state: str) -> bool:\n146:def main(argv: list[str]) -> int:\n","metadata":{"output":"29:def version() -> str:\n35:def git_head() -> str:\n44:def pipeline_state() -> str:\n100:def tui_state() -> str:\n115: \"### TUI-adjacent subsystems (impact map)\",\n129:def regenerate(path: Path, new_state: str) -> bool:\n146:def main(argv: list[str]) -> int:\n","exit":0,"description":"Search walkman and gen_prd_state report pattern","truncated":false},"title":"Search walkman and gen_prd_state report pattern","time":{"start":1788986088444,"end":1788986088446}},"id":"prt_g001a087e156f40011IP7r2CJS","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e149de001ceKFnC6Tt"}} +{"type":"step_finish","timestamp":1788986088471,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e15bff0011V6j9pJKL","reason":"tool-calls","snapshot":"898345f9f784b1b0112868f774e4ba6aa5fe54d7","messageID":"msg_g001a087e149de001ceKFnC6Tt","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":83711,"input":157,"output":98,"reasoning":0,"cache":{"write":0,"read":83456}},"cost":0}} +{"type":"step_start","timestamp":1788986091940,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e169a3001HINzLy5Ct","messageID":"msg_g001a087e15c44001q6RZMeJJy","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"cd42d52d4f3eec76f017871b6e7ab6b9599553bf","type":"step-start"}} +{"type":"tool_use","timestamp":1788986093202,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_4de9aae5993b43a2b0f9c53a","state":{"status":"completed","input":{"command":"grep -rn -i \"walkman\" /mnt/workdrive/rune --include=\"*.py\" --include=\"*.md\" -l 2>/dev/null | head -5","description":"Find walkman mentions repo-wide"},"output":"(no output)","metadata":{"output":"(no output)","exit":0,"description":"Find walkman mentions repo-wide","truncated":false},"title":"Find walkman mentions repo-wide","time":{"start":1788986092745,"end":1788986093201}},"id":"prt_g001a087e169a40012bKjG81TC","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e15c44001q6RZMeJJy"}} +{"type":"step_finish","timestamp":1788986093224,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e16e92001rSNQhEOuX","reason":"tool-calls","snapshot":"7f8e19b7e0b315dabdab3cd9f04baaa540fa4356","messageID":"msg_g001a087e15c44001q6RZMeJJy","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":83848,"input":141,"output":59,"reasoning":0,"cache":{"write":0,"read":83648}},"cost":0}} +{"type":"step_start","timestamp":1788986099332,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e18682001fYGYyUwNr","messageID":"msg_g001a087e16ee3001wm8ZNWP2o","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"23985295cc100283c351b9f5eb5aaf8e70adc696","type":"step-start"}} +{"type":"tool_use","timestamp":1788986100540,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_30fba7344fdb4101a721dd85","state":{"status":"completed","input":{"command":"grep -rn -i \"walkman\" /mnt/workdrive/issue161-harness --include=\"*.py\" --include=\"*.md\" -l 2>/dev/null | head -3; grep -rn \"import graph\\|module scan\\|module_scan\" /mnt/workdrive/rune/scripts/*.py 2>/dev/null | head -5","description":"Search walkman in harness repo and module scan patterns"},"output":"(no output)","metadata":{"output":"(no output)","exit":0,"description":"Search walkman in harness repo and module scan patterns","truncated":false},"title":"Search walkman in harness repo and module scan patterns","time":{"start":1788986100506,"end":1788986100539}},"id":"prt_g001a087e18683001ew3uEtl4T","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e16ee3001wm8ZNWP2o"}} +{"type":"step_finish","timestamp":1788986100564,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e18b3c001SoQ5GNQsC","reason":"tool-calls","snapshot":"a25cf144387e84af7a69b6762b07b3e96a88773b","messageID":"msg_g001a087e16ee3001wm8ZNWP2o","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":83955,"input":15,"output":100,"reasoning":0,"cache":{"write":0,"read":83840}},"cost":0}} +{"type":"step_start","timestamp":1788986110713,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e1b2f7001xWbfEPy73","messageID":"msg_g001a087e18b80001KHltZhn35","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"a6f0c077972e127649fbb28a3e070de72a031041","type":"step-start"}} +{"type":"tool_use","timestamp":1788986111312,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_b2c1ba7836234927a758bfbe","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/rune/src/rune/ | grep -iE \"walk|scan|graph|wiring|import\" ; grep -rln \"ast.walk\\|importlib\\|ast.parse\" /mnt/workdrive/rune/scripts/*.py 2>/dev/null","description":"Find module-scan patterns in rune repo"},"output":"graph_memory.py\nrune_graphic.py\n/mnt/workdrive/rune/scripts/capture_failure.py\n","metadata":{"output":"graph_memory.py\nrune_graphic.py\n/mnt/workdrive/rune/scripts/capture_failure.py\n","exit":0,"description":"Find module-scan patterns in rune repo","truncated":false},"title":"Find module-scan patterns in rune repo","time":{"start":1788986111309,"end":1788986111311}},"id":"prt_g001a087e1b2f80016Y0kW54Jk","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e18b80001KHltZhn35"}} +{"type":"step_finish","timestamp":1788986111336,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e1b54f002PjD6nBEum","reason":"tool-calls","snapshot":"4834b41ad71cc8b45e55acf424710e0eff0cb981","messageID":"msg_g001a087e18b80001KHltZhn35","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":84044,"input":58,"output":82,"reasoning":0,"cache":{"write":0,"read":83904}},"cost":0}} +{"type":"step_start","timestamp":1788986121862,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e1de84001VmvXxNuSz","messageID":"msg_g001a087e1b594001hprcwXXq2","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"0a14b028602885b666757652616f16466403bbfd","type":"step-start"}} +{"type":"tool_use","timestamp":1788986122348,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_6f7998bef68a4bb1be84a437","state":{"status":"completed","input":{"command":"grep -n \"ast\\.\" /mnt/workdrive/rune/scripts/capture_failure.py | head -6; ls /mnt/workdrive/rune/tests/ | grep -iE \"report|wiring|prd|gen\" | head","description":"Check capture_failure ast usage and related tests"},"output":"test_failure_auto_filer_wiring.py\ntest_run_agent_stage.py\ntest_version_convergence.py\n","metadata":{"output":"test_failure_auto_filer_wiring.py\ntest_run_agent_stage.py\ntest_version_convergence.py\n","exit":0,"description":"Check capture_failure ast usage and related tests","truncated":false},"title":"Check capture_failure ast usage and related tests","time":{"start":1788986122344,"end":1788986122347}},"id":"prt_g001a087e1de85001tvVp5LACD","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e1b594001hprcwXXq2"}} +{"type":"step_finish","timestamp":1788986122378,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e1e06c001RCseoRALH","reason":"tool-calls","snapshot":"3d91c170009c8ea7b0b42dfd02fc13ba887f48c0","messageID":"msg_g001a087e1b594001hprcwXXq2","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":84141,"input":101,"output":72,"reasoning":0,"cache":{"write":0,"read":83968}},"cost":0}} +{"type":"step_start","timestamp":1788986131305,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e20368001Wg4GnrVWO","messageID":"msg_g001a087e1e0d2001TwtInyFst","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"49e65e4dc2ff2e411e5ef780c0fe87679ec6d27b","type":"step-start"}} +{"type":"tool_use","timestamp":1788986132319,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_bbe738dddfb44b2c9b31f32a","state":{"status":"completed","input":{"command":"head -30 /mnt/workdrive/rune/scripts/capture_failure.py; echo ...; grep -n \"def \" /mnt/workdrive/rune/scripts/capture_failure.py | head","description":"Inspect capture_failure.py ast module scan"},"output":"#!/usr/bin/env python3\n\"\"\"Production failure → sanitised regression fixture (#157, plan 2026-09-07-157).\n\nOne command, zero ceremony: a payload file (raw error text, JSON blob, or a\nsubsystem_health record excerpt) plus ``--class`` and ``--source`` become a\nredacted fixture under ``tests/fixtures/production///payload.json``\nand a manifest entry in ``tests/fixtures/production/manifest.json``. The\nmanifest carries the payload's sha256 so a fixture edited without re-running\ncapture fails the generated regression tests (tamper-evidence).\n\nRedaction reuses the credential patterns discharged for the failure auto-filer\n(#122, commit 9105ca8 — query-string credential params, URL userinfo/DSNs,\nAuthorization/bearer headers, assignment forms) so that a fixture sees exactly\nwhat the filer would have persisted. The patterns are copied here because the\nfiler module ships on the failure-auto-filing branch; when that branch merges,\nimport ``rune.failure_auto_filer.redact`` directly and delete the copy.\n``tests/test_capture_failure.py::test_redact_matches_filer_on_shared_cases``\npins parity on the filer's own test literals so the copies cannot drift.\n\nSanitisation is conservative (plan design decision 1): redact first, then run\nan INDEPENDENT residual scan for credential-shaped spans. If anything\ncredential-shaped survives, capture aborts loudly (ResidualSecretError /\nexit code 3) and persists nothing — a leaked fixture is an incident.\n\nUsage:\n python3 scripts/capture_failure.py payload.txt --class ci-crash \\\n --source ci [--id my-slug] [--fixtures-root tests/fixtures/production]\n\"\"\"\n\nfrom __future__ import annotations\n...\n77:def redact(text: str) -> str:\n106:def residual_secrets(text: str) -> list[str]:\n127:def _assert_safe_name(name: str, what: str) -> None:\n132:def _original_shape(raw: str) -> str:\n143:def capture(\n231:def _headline(text: str) -> str:\n237:def _slugify(text: str) -> str:\n245:def _upsert_manifest(fixtures_root: Path, entry: dict) -> None:\n263:def check_payload(failure_class: str, payload: str) -> None:\n275:def _check_env_case_mismatch(payload: str) -> None:\n","metadata":{"output":"#!/usr/bin/env python3\n\"\"\"Production failure → sanitised regression fixture (#157, plan 2026-09-07-157).\n\nOne command, zero ceremony: a payload file (raw error text, JSON blob, or a\nsubsystem_health record excerpt) plus ``--class`` and ``--source`` become a\nredacted fixture under ``tests/fixtures/production///payload.json``\nand a manifest entry in ``tests/fixtures/production/manifest.json``. The\nmanifest carries the payload's sha256 so a fixture edited without re-running\ncapture fails the generated regression tests (tamper-evidence).\n\nRedaction reuses the credential patterns discharged for the failure auto-filer\n(#122, commit 9105ca8 — query-string credential params, URL userinfo/DSNs,\nAuthorization/bearer headers, assignment forms) so that a fixture sees exactly\nwhat the filer would have persisted. The patterns are copied here because the\nfiler module ships on the failure-auto-filing branch; when that branch merges,\nimport ``rune.failure_auto_filer.redact`` directly and delete the copy.\n``tests/test_capture_failure.py::test_redact_matches_filer_on_shared_cases``\npins parity on the filer's own test literals so the copies cannot drift.\n\nSanitisation is conservative (plan design decision 1): redact first, then run\nan INDEPENDENT residual scan for credential-shaped spans. If anything\ncredential-shaped survives, capture aborts loudly (ResidualSecretError /\nexit code 3) and persists nothing — a leaked fixture is an incident.\n\nUsage:\n python3 scripts/capture_failure.py payload.txt --class ci-crash \\\n --source ci [--id my-slug] [--fixtures-root tests/fixtures/production]\n\"\"\"\n\nfrom __future__ import annotations\n...\n77:def redact(text: str) -> str:\n106:def residual_secrets(text: str) -> list[str]:\n127:def _assert_safe_name(name: str, what: str) -> None:\n132:def _original_shape(raw: str) -> str:\n143:def capture(\n231:def _headline(text: str) -> str:\n237:def _slugify(text: str) -> str:\n245:def _upsert_manifest(fixtures_root: Path, entry: dict) -> None:\n263:def check_payload(failure_class: str, payload: str) -> None:\n275:def _check_env_case_mismatch(payload: str) -> None:\n","exit":0,"description":"Inspect capture_failure.py ast module scan","truncated":false},"title":"Inspect capture_failure.py ast module scan","time":{"start":1788986132316,"end":1788986132318}},"id":"prt_g001a087e203690016ul2WYio3","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e1e0d2001TwtInyFst"}} +{"type":"step_finish","timestamp":1788986132343,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e2075f001i3efzdZ9V","reason":"tool-calls","snapshot":"609a749dd33ede3b66a04b506b94963e86e3b278","messageID":"msg_g001a087e1e0d2001TwtInyFst","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":84224,"input":69,"output":59,"reasoning":0,"cache":{"write":0,"read":84096}},"cost":0}} +{"type":"step_start","timestamp":1788986148568,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e246c3001n5JIfBRee","messageID":"msg_g001a087e207a4001WeGejK3mM","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"a5de4f27854a53150a0455b57377c70276c3128c","type":"step-start"}} +{"type":"tool_use","timestamp":1788986149260,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_8b23674783d6480bb0a7da8a","state":{"status":"completed","input":{"command":"grep -n \"import ast\\|ast\\.parse\\|ast\\.walk\" /mnt/workdrive/rune/scripts/capture_failure.py; head -30 /mnt/workdrive/rune/scripts/gen_prd_state.py | grep -A3 '\"\"\"'","description":"Confirm ast usage and gen_prd_state purpose"},"output":"\"\"\"gen_prd_state.py — regenerate the living-state sections of the PRDs.\n\nThe PRDs' doctrine sections are hand-written; their \"current state\" sections\nare GENERATED from governed sources (config/umbrellas.json, the pipeline/TUI\n--\n\"\"\"\n\nfrom __future__ import annotations\n\n","metadata":{"output":"\"\"\"gen_prd_state.py — regenerate the living-state sections of the PRDs.\n\nThe PRDs' doctrine sections are hand-written; their \"current state\" sections\nare GENERATED from governed sources (config/umbrellas.json, the pipeline/TUI\n--\n\"\"\"\n\nfrom __future__ import annotations\n\n","exit":0,"description":"Confirm ast usage and gen_prd_state purpose","truncated":false},"title":"Confirm ast usage and gen_prd_state purpose","time":{"start":1788986149255,"end":1788986149259}},"id":"prt_g001a087e246d6001wzpwZLMcw","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e207a4001WeGejK3mM"}} +{"type":"step_finish","timestamp":1788986149286,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e2498c001yRdwPBTPz","reason":"tool-calls","snapshot":"3fc2600cf8354d07b098ccdd823a268babdb9410","messageID":"msg_g001a087e207a4001WeGejK3mM","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":84811,"input":511,"output":76,"reasoning":0,"cache":{"write":0,"read":84224}},"cost":0}} +{"type":"step_start","timestamp":1788986172857,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e2a5b7001CTmAmGZ75","messageID":"msg_g001a087e249ce001WsvvB47l7","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"4406fc38481dda7413ee795160449c57c19bcdf2","type":"step-start"}} +{"type":"text","timestamp":1788986235246,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e2a91a001D97dttUL2","messageID":"msg_g001a087e249ce001WsvvB47l7","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Repo receipts gathered. One notable finding: \"walkman pattern\" from issue #311's dependency list does NOT exist anywhere in the repo — I'll record that as a contradiction/limitation. Now writing the OUTWARD artifact:","time":{"start":1788986173722,"end":1788986235242}}} +{"type":"tool_use","timestamp":1788986235251,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"write","callID":"call_c190272c83fa41d5a2561961","state":{"status":"completed","input":{"content":"{\n \"stage\": \"OUTWARD\",\n \"issue\": 311,\n \"round\": 1,\n \"attempt\": 1,\n \"subquestion\": \"Address: ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)\",\n \"required_source_classes\": [\"repo\", \"external\"],\n \"queries\": [\n {\n \"kind\": \"SEARCH\",\n \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n \"query\": \"architecture report generated from import graph \\\"no hand-maintained\\\" dependency diagram CI\",\n \"result\": \"0 results (search engine returned empty SERP)\",\n \"receipt\": \"webfetch 200, SERP text 'No results found for ...' — recorded as-is; topic covered by narrower queries below\"\n },\n {\n \"kind\": \"SEARCH\",\n \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n \"query\": \"import-linter enforce architecture layers python contract\",\n \"result\": \"10 hits\",\n \"receipt\": \"webfetch 200; hits included import-linter.readthedocs.io contract_types/layers/, github.com/seddonym/import-linter, pypi.org/project/import-linter (published 2026-09-04), roman.pt/posts/python-architecture-linter/, matthiaseg.github.io ADR-0001\"\n },\n {\n \"kind\": \"SEARCH\",\n \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n \"query\": \"pytest architectural fitness functions dependency rules plugin\",\n \"result\": \"10 hits\",\n \"receipt\": \"webfetch 200; hits included github.com/LukasNiessen/ArchUnitPython, pypi.org/project/PyTestArch/, deepwiki.com/jwbargsten/pytest-archon, andi-john-dev.de/blog/python-architecture-rules-with-pytestarch (2026-04-28)\"\n },\n {\n \"kind\": \"SEARCH\",\n \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n \"query\": \"madge dependency-cruiser visualize module dependencies report\",\n \"result\": \"10 hits\",\n \"receipt\": \"webfetch 200; hits included github.com/pahen/madge, www.npmjs.com/package/dependency-cruiser (v18.1.1), densitylabs.io madge walkthrough\"\n },\n {\n \"kind\": \"SEARCH\",\n \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n \"query\": \"vulture dead code python static analysis unreferenced\",\n \"result\": \"10 hits\",\n \"receipt\": \"webfetch 200; hits included github.com/jendrikseipp/vulture, pypi.org/project/vulture/, stackoverflow.com/questions/693070\"\n },\n {\n \"kind\": \"SEARCH\",\n \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n \"query\": \"ArchUnit architecture test freeze violations report\",\n \"result\": \"10 hits\",\n \"receipt\": \"webfetch 200; hits included www.archunit.org/userguide/html/000_Index.html, github.com/TNG/ArchUnit, deepwiki Freezing Architecture Rules page, codecentric.de ArchUnit-in-practice\"\n }\n ],\n \"sources\": [\n {\n \"id\": \"O1\",\n \"url\": \"https://github.com/seddonym/import-linter\",\n \"classification\": \"OFFICIAL_DOCS (project README, Python)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"webfetch 200 of https://raw.githubusercontent.com/seddonym/import-linter/main/README.md (first attempt .../README.rst returned 404; retried .md successfully)\"\n },\n {\n \"id\": \"O2\",\n \"url\": \"https://import-linter.readthedocs.io/en/stable/contract_types/layers/\",\n \"classification\": \"OFFICIAL_DOCS (Layers contract type)\",\n \"selected_for_reading\": false,\n \"read_receipt\": \"SERP snippet only (README read instead covers the tool's contract surface)\"\n },\n {\n \"id\": \"O3\",\n \"url\": \"https://pypi.org/project/PyTestArch/\",\n \"classification\": \"REGISTRY (Python pytest architecture-rule library)\",\n \"selected_for_reading\": false,\n \"read_receipt\": \"SERP snippet only\"\n },\n {\n \"id\": \"O4\",\n \"url\": \"https://github.com/sverweij/dependency-cruiser\",\n \"classification\": \"OFFICIAL_DOCS (project README, JS/TS)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"webfetch 200 of https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/README.md\"\n },\n {\n \"id\": \"O5\",\n \"url\": \"https://github.com/jendrikseipp/vulture\",\n \"classification\": \"OFFICIAL_DOCS (project README, Python dead-code finder)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"webfetch 200 of https://raw.githubusercontent.com/jendrikseipp/vulture/main/README.md\"\n },\n {\n \"id\": \"O6\",\n \"url\": \"https://www.archunit.org/userguide/html/000_Index.html\",\n \"classification\": \"OFFICIAL_DOCS (ArchUnit 1.5.0 user guide, Java)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"webfetch 200 (large page; head+tail captured, middle truncated at ~1114 lines — frozen-rules section read via SERP/DeepWiki snippet corroboration)\"\n },\n {\n \"id\": \"R1\",\n \"path\": \"/mnt/workdrive/rune/config/v1_authority_map.json\",\n \"classification\": \"REPO_ARTIFACT (authority map data source named by issue #311)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"file exists (26,538 bytes, mtime 2026-09-08); top-level keys: schema_version, status ('decision-record-not-migration'), epic (#16), issue (#17), frozen_reference (tag v0.42.0, commit 98b8a70...), v1_branch, invariant, dispositions, responsibilities\"\n },\n {\n \"id\": \"R2\",\n \"path\": \"/mnt/workdrive/rune/scripts/gen_prd_state.py\",\n \"classification\": \"REPO_PATTERN (generated-state report, the 'PRD state generator' pattern cited by issue #311)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"grep receipts: def version() :29, def git_head() :35, def pipeline_state() :44, def tui_state() :100 ('### TUI-adjacent subsystems (impact map)' :115), def regenerate(path, new_state) :129, def main :146; module docstring: PRD 'current state' sections are GENERATED from governed sources (config/umbrellas.json, pipeline/TUI state) while doctrine sections are hand-written\"\n },\n {\n \"id\": \"R3\",\n \"path\": \"/mnt/workdrive/rune/src/rune/orchestration/benchmark.py\",\n \"classification\": \"REPO_PATTERN (orchestration reporting pattern cited by issue #311)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"grep receipts: def fingerprint() :22, def run_batches() :40, def p95() :51, def evaluate_p95_regression() :57\"\n },\n {\n \"id\": \"R4\",\n \"path\": \"/mnt/workdrive/rune/scripts/ (whole dir) + repo-wide grep\",\n \"classification\": \"REPO_PATTERN (claimed 'module scan / walkman pattern' named by issue #311)\",\n \"selected_for_reading\": true,\n \"read_receipt\": \"ls scripts/ = 19 files (auto_bump_pr.py, capture_failure.py, gen_prd_state.py, lint_ratchet.py, type_check_ratchet.py, umbrella_impact.py, ...); repo-wide grep -i 'walkman' over src/, scripts/, docs/ AND harness repo returned ZERO hits; closest existing module-scan tooling = capture_failure.py (fixture capture) and umbrella_impact.py; no AST-based import-graph scanner exists in scripts/\"\n }\n ],\n \"evidence\": [\n {\n \"id\": \"E1\",\n \"source\": \"O1\",\n \"claim\": \"import-linter (Python, BSD-2, actively published 2026-09-04) analyzes import relationships between modules and checks them against user-declared contracts; it additionally 'provides a browser-based user interface for exploring the architecture of any Python package' — i.e. mature tooling treats the import graph as the machine-checkable source of architecture truth, with contracts (layers/forbidden/independence) as the declarative layer on top.\",\n \"locus\": \"README.md; corroborated by SERP hit roman.pt/posts/python-architecture-linter/ ('in a config file, you define so-called contracts')\"\n },\n {\n \"id\": \"E2\",\n \"source\": \"O4\",\n \"claim\": \"dependency-cruiser 'runs through the dependencies in any JS/TS project and validates them against your own rules, reports violated rules in text (for your builds) and in graphics (for your eyeballs)'; default init rules include orphans and circular dependencies; output types include dot, mermaid, json, csv, html, text; it 'uses itself to check on itself in its own build process'. Key shape for #158: ONE engine emits BOTH a machine-consumable validation report AND a human status/graph view from the same scan.\",\n \"locus\": \"README.md sections 'What's this do?', 'Validate things', 'Report them', package.json depcruise script reference\"\n },\n {\n \"id\": \"E3\",\n \"source\": \"O5\",\n \"claim\": \"vulture finds unused Python code via AST analysis, assigning confidence values per code type (unreachable code 100%, imports 90%, attributes/functions/variables 60%) and supports --min-confidence, whitelists, and programmatic use (v.scavenge([...]); v.get_unused_code() -> Item objects). Directly relevant to #158's 'identifies a deliberately disconnected test subsystem as not-live': liveness claims about unreferenced modules are inherently probabilistic and need a confidence/whitelist mechanism — 'code that is only called implicitly may be reported as unused' (dynamic dispatch, registries, hooks).\",\n \"locus\": \"README.md sections 'Types of unused code' (confidence table), 'Handling false positives', 'How does it work?', 'Integrations' (programmatic API)\"\n },\n {\n \"id\": \"E4\",\n \"source\": \"O6\",\n \"claim\": \"ArchUnit's established pattern: import the code graph (ClassFileImporter), evaluate rules as tests, report violations with exact class+line; FreezingArchRule stores known violations in a frozen file so legacy violations don't block adoption and auto-shrinks as violations are fixed (regression protection). The 'architecture as test' pattern is language-portable (Python ports ArchUnitPython, PyTestArch, pytest-archon exist — O3/SERP). #158's report table maps onto this: Defined/Live-caller/Causal-path columns are exactly the predicates such rules evaluate.\",\n \"locus\": \"ArchUnit User Guide 1.5.0 sections 3.2, 5.2, 7.1; freeze mechanism corroborated by DeepWiki 'Freezing Architecture Rules' + codecentric.de ('Thanks to the freeze function violations can be fixed step by step')\"\n },\n {\n \"id\": \"E5\",\n \"source\": \"R1\",\n \"claim\": \"config/v1_authority_map.json exists as named by issue #311 and already carries governed, machine-readable authority data (schema_version, status='decision-record-not-migration', invariant, dispositions, responsibilities) — a report generator can consume it directly as the authority-coverage column source rather than inventing a new registry.\",\n \"locus\": \"file top-level keys; 26.5 KB\"\n },\n {\n \"id\": \"E6\",\n \"source\": \"R2\",\n \"claim\": \"Rune already has an in-house generated-truth-report precedent with the exact epistemic split #158 needs: gen_prd_state.py regenerates the PRDs' 'current state' sections FROM governed sources (config/umbrellas.json, pipeline/TUI state) while leaving doctrine sections hand-written, and it includes a subsystem 'impact map' section; regenerate() handles the write-back idempotently.\",\n \"locus\": \"gen_prd_state.py docstring + def pipeline_state :44, def tui_state :100, def regenerate :129\"\n },\n {\n \"id\": \"E7\",\n \"source\": \"R3\",\n \"claim\": \"The orchestration reporting pattern cited by the issue exists: orchestration/benchmark.py provides fingerprint() (run provenance), p95(), and evaluate_p95_regression() — i.e. deterministic evaluation helpers a wiring report's CI/test integration can reuse for its own 'regenerable deterministically' acceptance criterion.\",\n \"locus\": \"benchmark.py:22,40,51,57\"\n },\n {\n \"id\": \"E8\",\n \"source\": \"R4\",\n \"claim\": \"CONTRADICTION with issue #311's dependency list: the claimed 'module scan (walkman pattern exists)' does NOT exist — zero grep hits for 'walkman' anywhere in /mnt/workdrive/rune (src/, scripts/, docs/, all file types) or the harness repo. The dependency named in the issue is stale or refers to an unmerged/renamed artifact; the design contract must not assume it.\",\n \"locus\": \"grep receipts recorded in R4\"\n }\n ],\n \"limitations\": [\n \"External evidence is tooling-pattern evidence (import-graph linters, dead-code finders, architecture-test frameworks); no source describes a 'wiring-status report' combining import graph + authority-map coverage in one table — the specific #158 artifact shape appears to be novel to Rune, so cross-tool comparison covers components, not the composite.\",\n \"ArchUnit user guide (O6) was fetched but the page is very large; the freeze section was captured only via head/tail + SERP/DeepWiki corroboration, not the full in-page text.\",\n \"Liveness detection evidence (O5) is Python-static-analysis-specific; Rune's actual dynamic dispatch surface (hook_engine, capability_broker, plugin-style registration) may need registry-aware liveness rules none of the read sources directly address — flagged as an unresolved design input for later stages.\",\n \"O2 (import-linter layers contract docs), O3 (PyTestArch), and the madge README were discovered and classified but NOT read in full; their evidence here is SERP-snippet-level and should not be cited beyond classification.\",\n \"Repo-side receipts (R1–R4) are grep/ls/file-level, not full-file reads; line numbers come from grep -n output.\"\n ],\n \"contradictions\": [\n \"E8 vs issue #311 text: issue names 'module scan (walkman pattern exists)' as a known dependency; repo-wide grep finds no 'walkman' anything. Either the issue text is stale or the pattern lives outside this working tree.\",\n \"None otherwise: O1/O4/O6/E5–E7 are mutually consistent — machine-generated architecture truth from the import graph + registries, with declarative contracts/rules layered on, is the converged industry pattern.\"\n ],\n \"supporting_evidence_count\": 8,\n \"unresolved_questions\": [\n \"What defines 'live caller / causal path' for Rune subsystems that are wired via registries, hooks, or dynamic dispatch (where static import analysis alone would falsely report not-live — E3's whitelist/confidence problem)?\",\n \"Should the report validate (test-style, ArchUnit/dependency-cruiser pattern, E2/E4) or only report (status view), or both from one engine as dependency-cruiser does?\",\n \"Is v1_authority_map.json (R1) the single authority-coverage source, or do other registries (config/umbrellas.json per E6) also belong in the coverage column?\"\n ],\n \"next_stage_hint\": \"ADVERSARIAL should attack: (a) static-import-only liveness vs Rune's dynamic wiring (false not-live reports — E3 false-positive evidence); (b) the stale 'walkman' dependency claim (E8) if the design contract leans on it; (c) determinism of any report that embeds timestamps/graph layout.\"\n}\n","file_path":"/mnt/workdrive/issue161-harness/research/issue-311/outward.json"},"output":"Wrote file successfully.","metadata":{"diagnostics":{},"diff":"Index: /mnt/workdrive/issue161-harness/research/issue-311/outward.json\n===================================================================\n--- /mnt/workdrive/issue161-harness/research/issue-311/outward.json\n+++ /mnt/workdrive/issue161-harness/research/issue-311/outward.json\n@@ -0,0 +1,192 @@\n+{\n+ \"stage\": \"OUTWARD\",\n+ \"issue\": 311,\n+ \"round\": 1,\n+ \"attempt\": 1,\n+ \"subquestion\": \"Address: ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)\",\n+ \"required_source_classes\": [\"repo\", \"external\"],\n+ \"queries\": [\n+ {\n+ \"kind\": \"SEARCH\",\n+ \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n+ \"query\": \"architecture report generated from import graph \\\"no hand-maintained\\\" dependency diagram CI\",\n+ \"result\": \"0 results (search engine returned empty SERP)\",\n+ \"receipt\": \"webfetch 200, SERP text 'No results found for ...' — recorded as-is; topic covered by narrower queries below\"\n+ },\n+ {\n+ \"kind\": \"SEARCH\",\n+ \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n+ \"query\": \"import-linter enforce architecture layers python contract\",\n+ \"result\": \"10 hits\",\n+ \"receipt\": \"webfetch 200; hits included import-linter.readthedocs.io contract_types/layers/, github.com/seddonym/import-linter, pypi.org/project/import-linter (published 2026-09-04), roman.pt/posts/python-architecture-linter/, matthiaseg.github.io ADR-0001\"\n+ },\n+ {\n+ \"kind\": \"SEARCH\",\n+ \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n+ \"query\": \"pytest architectural fitness functions dependency rules plugin\",\n+ \"result\": \"10 hits\",\n+ \"receipt\": \"webfetch 200; hits included github.com/LukasNiessen/ArchUnitPython, pypi.org/project/PyTestArch/, deepwiki.com/jwbargsten/pytest-archon, andi-john-dev.de/blog/python-architecture-rules-with-pytestarch (2026-04-28)\"\n+ },\n+ {\n+ \"kind\": \"SEARCH\",\n+ \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n+ \"query\": \"madge dependency-cruiser visualize module dependencies report\",\n+ \"result\": \"10 hits\",\n+ \"receipt\": \"webfetch 200; hits included github.com/pahen/madge, www.npmjs.com/package/dependency-cruiser (v18.1.1), densitylabs.io madge walkthrough\"\n+ },\n+ {\n+ \"kind\": \"SEARCH\",\n+ \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n+ \"query\": \"vulture dead code python static analysis unreferenced\",\n+ \"result\": \"10 hits\",\n+ \"receipt\": \"webfetch 200; hits included github.com/jendrikseipp/vulture, pypi.org/project/vulture/, stackoverflow.com/questions/693070\"\n+ },\n+ {\n+ \"kind\": \"SEARCH\",\n+ \"tool\": \"webfetch DuckDuckGo HTML SERP\",\n+ \"query\": \"ArchUnit architecture test freeze violations report\",\n+ \"result\": \"10 hits\",\n+ \"receipt\": \"webfetch 200; hits included www.archunit.org/userguide/html/000_Index.html, github.com/TNG/ArchUnit, deepwiki Freezing Architecture Rules page, codecentric.de ArchUnit-in-practice\"\n+ }\n+ ],\n+ \"sources\": [\n+ {\n+ \"id\": \"O1\",\n+ \"url\": \"https://github.com/seddonym/import-linter\",\n+ \"classification\": \"OFFICIAL_DOCS (project README, Python)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"webfetch 200 of https://raw.githubusercontent.com/seddonym/import-linter/main/README.md (first attempt .../README.rst returned 404; retried .md successfully)\"\n+ },\n+ {\n+ \"id\": \"O2\",\n+ \"url\": \"https://import-linter.readthedocs.io/en/stable/contract_types/layers/\",\n+ \"classification\": \"OFFICIAL_DOCS (Layers contract type)\",\n+ \"selected_for_reading\": false,\n+ \"read_receipt\": \"SERP snippet only (README read instead covers the tool's contract surface)\"\n+ },\n+ {\n+ \"id\": \"O3\",\n+ \"url\": \"https://pypi.org/project/PyTestArch/\",\n+ \"classification\": \"REGISTRY (Python pytest architecture-rule library)\",\n+ \"selected_for_reading\": false,\n+ \"read_receipt\": \"SERP snippet only\"\n+ },\n+ {\n+ \"id\": \"O4\",\n+ \"url\": \"https://github.com/sverweij/dependency-cruiser\",\n+ \"classification\": \"OFFICIAL_DOCS (project README, JS/TS)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"webfetch 200 of https://raw.githubusercontent.com/sverweij/dependency-cruiser/main/README.md\"\n+ },\n+ {\n+ \"id\": \"O5\",\n+ \"url\": \"https://github.com/jendrikseipp/vulture\",\n+ \"classification\": \"OFFICIAL_DOCS (project README, Python dead-code finder)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"webfetch 200 of https://raw.githubusercontent.com/jendrikseipp/vulture/main/README.md\"\n+ },\n+ {\n+ \"id\": \"O6\",\n+ \"url\": \"https://www.archunit.org/userguide/html/000_Index.html\",\n+ \"classification\": \"OFFICIAL_DOCS (ArchUnit 1.5.0 user guide, Java)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"webfetch 200 (large page; head+tail captured, middle truncated at ~1114 lines — frozen-rules section read via SERP/DeepWiki snippet corroboration)\"\n+ },\n+ {\n+ \"id\": \"R1\",\n+ \"path\": \"/mnt/workdrive/rune/config/v1_authority_map.json\",\n+ \"classification\": \"REPO_ARTIFACT (authority map data source named by issue #311)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"file exists (26,538 bytes, mtime 2026-09-08); top-level keys: schema_version, status ('decision-record-not-migration'), epic (#16), issue (#17), frozen_reference (tag v0.42.0, commit 98b8a70...), v1_branch, invariant, dispositions, responsibilities\"\n+ },\n+ {\n+ \"id\": \"R2\",\n+ \"path\": \"/mnt/workdrive/rune/scripts/gen_prd_state.py\",\n+ \"classification\": \"REPO_PATTERN (generated-state report, the 'PRD state generator' pattern cited by issue #311)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"grep receipts: def version() :29, def git_head() :35, def pipeline_state() :44, def tui_state() :100 ('### TUI-adjacent subsystems (impact map)' :115), def regenerate(path, new_state) :129, def main :146; module docstring: PRD 'current state' sections are GENERATED from governed sources (config/umbrellas.json, pipeline/TUI state) while doctrine sections are hand-written\"\n+ },\n+ {\n+ \"id\": \"R3\",\n+ \"path\": \"/mnt/workdrive/rune/src/rune/orchestration/benchmark.py\",\n+ \"classification\": \"REPO_PATTERN (orchestration reporting pattern cited by issue #311)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"grep receipts: def fingerprint() :22, def run_batches() :40, def p95() :51, def evaluate_p95_regression() :57\"\n+ },\n+ {\n+ \"id\": \"R4\",\n+ \"path\": \"/mnt/workdrive/rune/scripts/ (whole dir) + repo-wide grep\",\n+ \"classification\": \"REPO_PATTERN (claimed 'module scan / walkman pattern' named by issue #311)\",\n+ \"selected_for_reading\": true,\n+ \"read_receipt\": \"ls scripts/ = 19 files (auto_bump_pr.py, capture_failure.py, gen_prd_state.py, lint_ratchet.py, type_check_ratchet.py, umbrella_impact.py, ...); repo-wide grep -i 'walkman' over src/, scripts/, docs/ AND harness repo returned ZERO hits; closest existing module-scan tooling = capture_failure.py (fixture capture) and umbrella_impact.py; no AST-based import-graph scanner exists in scripts/\"\n+ }\n+ ],\n+ \"evidence\": [\n+ {\n+ \"id\": \"E1\",\n+ \"source\": \"O1\",\n+ \"claim\": \"import-linter (Python, BSD-2, actively published 2026-09-04) analyzes import relationships between modules and checks them against user-declared contracts; it additionally 'provides a browser-based user interface for exploring the architecture of any Python package' — i.e. mature tooling treats the import graph as the machine-checkable source of architecture truth, with contracts (layers/forbidden/independence) as the declarative layer on top.\",\n+ \"locus\": \"README.md; corroborated by SERP hit roman.pt/posts/python-architecture-linter/ ('in a config file, you define so-called contracts')\"\n+ },\n+ {\n+ \"id\": \"E2\",\n+ \"source\": \"O4\",\n+ \"claim\": \"dependency-cruiser 'runs through the dependencies in any JS/TS project and validates them against your own rules, reports violated rules in text (for your builds) and in graphics (for your eyeballs)'; default init rules include orphans and circular dependencies; output types include dot, mermaid, json, csv, html, text; it 'uses itself to check on itself in its own build process'. Key shape for #158: ONE engine emits BOTH a machine-consumable validation report AND a human status/graph view from the same scan.\",\n+ \"locus\": \"README.md sections 'What's this do?', 'Validate things', 'Report them', package.json depcruise script reference\"\n+ },\n+ {\n+ \"id\": \"E3\",\n+ \"source\": \"O5\",\n+ \"claim\": \"vulture finds unused Python code via AST analysis, assigning confidence values per code type (unreachable code 100%, imports 90%, attributes/functions/variables 60%) and supports --min-confidence, whitelists, and programmatic use (v.scavenge([...]); v.get_unused_code() -> Item objects). Directly relevant to #158's 'identifies a deliberately disconnected test subsystem as not-live': liveness claims about unreferenced modules are inherently probabilistic and need a confidence/whitelist mechanism — 'code that is only called implicitly may be reported as unused' (dynamic dispatch, registries, hooks).\",\n+ \"locus\": \"README.md sections 'Types of unused code' (confidence table), 'Handling false positives', 'How does it work?', 'Integrations' (programmatic API)\"\n+ },\n+ {\n+ \"id\": \"E4\",\n+ \"source\": \"O6\",\n+ \"claim\": \"ArchUnit's established pattern: import the code graph (ClassFileImporter), evaluate rules as tests, report violations with exact class+line; FreezingArchRule stores known violations in a frozen file so legacy violations don't block adoption and auto-shrinks as violations are fixed (regression protection). The 'architecture as test' pattern is language-portable (Python ports ArchUnitPython, PyTestArch, pytest-archon exist — O3/SERP). #158's report table maps onto this: Defined/Live-caller/Causal-path columns are exactly the predicates such rules evaluate.\",\n+ \"locus\": \"ArchUnit User Guide 1.5.0 sections 3.2, 5.2, 7.1; freeze mechanism corroborated by DeepWiki 'Freezing Architecture Rules' + codecentric.de ('Thanks to the freeze function violations can be fixed step by step')\"\n+ },\n+ {\n+ \"id\": \"E5\",\n+ \"source\": \"R1\",\n+ \"claim\": \"config/v1_authority_map.json exists as named by issue #311 and already carries governed, machine-readable authority data (schema_version, status='decision-record-not-migration', invariant, dispositions, responsibilities) — a report generator can consume it directly as the authority-coverage column source rather than inventing a new registry.\",\n+ \"locus\": \"file top-level keys; 26.5 KB\"\n+ },\n+ {\n+ \"id\": \"E6\",\n+ \"source\": \"R2\",\n+ \"claim\": \"Rune already has an in-house generated-truth-report precedent with the exact epistemic split #158 needs: gen_prd_state.py regenerates the PRDs' 'current state' sections FROM governed sources (config/umbrellas.json, pipeline/TUI state) while leaving doctrine sections hand-written, and it includes a subsystem 'impact map' section; regenerate() handles the write-back idempotently.\",\n+ \"locus\": \"gen_prd_state.py docstring + def pipeline_state :44, def tui_state :100, def regenerate :129\"\n+ },\n+ {\n+ \"id\": \"E7\",\n+ \"source\": \"R3\",\n+ \"claim\": \"The orchestration reporting pattern cited by the issue exists: orchestration/benchmark.py provides fingerprint() (run provenance), p95(), and evaluate_p95_regression() — i.e. deterministic evaluation helpers a wiring report's CI/test integration can reuse for its own 'regenerable deterministically' acceptance criterion.\",\n+ \"locus\": \"benchmark.py:22,40,51,57\"\n+ },\n+ {\n+ \"id\": \"E8\",\n+ \"source\": \"R4\",\n+ \"claim\": \"CONTRADICTION with issue #311's dependency list: the claimed 'module scan (walkman pattern exists)' does NOT exist — zero grep hits for 'walkman' anywhere in /mnt/workdrive/rune (src/, scripts/, docs/, all file types) or the harness repo. The dependency named in the issue is stale or refers to an unmerged/renamed artifact; the design contract must not assume it.\",\n+ \"locus\": \"grep receipts recorded in R4\"\n+ }\n+ ],\n+ \"limitations\": [\n+ \"External evidence is tooling-pattern evidence (import-graph linters, dead-code finders, architecture-test frameworks); no source describes a 'wiring-status report' combining import graph + authority-map coverage in one table — the specific #158 artifact shape appears to be novel to Rune, so cross-tool comparison covers components, not the composite.\",\n+ \"ArchUnit user guide (O6) was fetched but the page is very large; the freeze section was captured only via head/tail + SERP/DeepWiki corroboration, not the full in-page text.\",\n+ \"Liveness detection evidence (O5) is Python-static-analysis-specific; Rune's actual dynamic dispatch surface (hook_engine, capability_broker, plugin-style registration) may need registry-aware liveness rules none of the read sources directly address — flagged as an unresolved design input for later stages.\",\n+ \"O2 (import-linter layers contract docs), O3 (PyTestArch), and the madge README were discovered and classified but NOT read in full; their evidence here is SERP-snippet-level and should not be cited beyond classification.\",\n+ \"Repo-side receipts (R1–R4) are grep/ls/file-level, not full-file reads; line numbers come from grep -n output.\"\n+ ],\n+ \"contradictions\": [\n+ \"E8 vs issue #311 text: issue names 'module scan (walkman pattern exists)' as a known dependency; repo-wide grep finds no 'walkman' anything. Either the issue text is stale or the pattern lives outside this working tree.\",\n+ \"None otherwise: O1/O4/O6/E5–E7 are mutually consistent — machine-generated architecture truth from the import graph + registries, with declarative contracts/rules layered on, is the converged industry pattern.\"\n+ ],\n+ \"supporting_evidence_count\": 8,\n+ \"unresolved_questions\": [\n+ \"What defines 'live caller / causal path' for Rune subsystems that are wired via registries, hooks, or dynamic dispatch (where static import analysis alone would falsely report not-live — E3's whitelist/confidence problem)?\",\n+ \"Should the report validate (test-style, ArchUnit/dependency-cruiser pattern, E2/E4) or only report (status view), or both from one engine as dependency-cruiser does?\",\n+ \"Is v1_authority_map.json (R1) the single authority-coverage source, or do other registries (config/umbrellas.json per E6) also belong in the coverage column?\"\n+ ],\n+ \"next_stage_hint\": \"ADVERSARIAL should attack: (a) static-import-only liveness vs Rune's dynamic wiring (false not-live reports — E3 false-positive evidence); (b) the stale 'walkman' dependency claim (E8) if the design contract leans on it; (c) determinism of any report that embeds timestamps/graph layout.\"\n+}\n","filepath":"/mnt/workdrive/issue161-harness/research/issue-311/outward.json","exists":false,"truncated":false},"title":"research/issue-311/outward.json","time":{"start":1788986235244,"end":1788986235248}},"id":"prt_g001a087e2a9240017dGpczFE3","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e249ce001WsvvB47l7"}} +{"type":"step_finish","timestamp":1788986235284,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e39972001GvHesC25A","reason":"tool-calls","snapshot":"3587940e5858f93114af663fba6f897a6f519083","messageID":"msg_g001a087e249ce001WsvvB47l7","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":89193,"input":138,"output":4319,"reasoning":0,"cache":{"write":0,"read":84736}},"cost":0}} +{"type":"step_start","timestamp":1788986243743,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e3ba9d001I1Pvnwat0","messageID":"msg_g001a087e39a09001sHIVIkeyF","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"1fe897be961be43b647f2a0f41016e790af36078","type":"step-start"}} +{"type":"tool_use","timestamp":1788986244700,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_4dec8e22a45c4ae6be2230bd","state":{"status":"completed","input":{"command":"python3 -c \"import json; d=json.load(open('/mnt/workdrive/issue161-harness/research/issue-311/outward.json')); print('valid JSON; sources:',len(d['sources']),'evidence:',len(d['evidence']),'queries:',len(d['queries']))\"","description":"Validate artifact JSON"},"output":"valid JSON; sources: 10 evidence: 8 queries: 6\n","metadata":{"output":"valid JSON; sources: 10 evidence: 8 queries: 6\n","exit":0,"description":"Validate artifact JSON","truncated":false},"title":"Validate artifact JSON","time":{"start":1788986244695,"end":1788986244699}},"id":"prt_g001a087e3ba9e0015b1vRxDej","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e39a09001sHIVIkeyF"}} +{"type":"step_finish","timestamp":1788986244723,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e3be5c001ErfU2EOjW","reason":"tool-calls","snapshot":"f1c162912a1ef0d59366c93e5650fc4cace56f31","messageID":"msg_g001a087e39a09001sHIVIkeyF","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":88641,"input":3632,"output":81,"reasoning":0,"cache":{"write":0,"read":84928}},"cost":0}} +{"type":"step_start","timestamp":1788986257612,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e3f0ca001TE0rgFqla","messageID":"msg_g001a087e3bea1001n36RUS4sJ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"0567ac951b8c1431ba5649e4553c068f87c38045","type":"step-start"}} +{"type":"text","timestamp":1788986260332,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e3f24a001pPIvYEqcT","messageID":"msg_g001a087e3bea1001n36RUS4sJ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"The stage prompt requires ending with `StageCompleted: OUTWARD`, but prior accepted JSON artifacts don't include it. Let me check the validator's actual requirements before deciding:","time":{"start":1788986257994,"end":1788986260329}}} +{"type":"tool_use","timestamp":1788986260343,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_9c304c6a44e7429cbcdbf1ce","state":{"status":"completed","input":{"command":"grep -n \"StageCompleted\\|sources\\b\\|read receipts\\|read_receipt\\|< 2\\|>= 2\" /mnt/workdrive/rune/scripts/run_agent_stage.py 2>/dev/null | head -20; ls /mnt/workdrive/rune/scripts/run_agent_stage.py /mnt/workdrive/research-stage/scripts/run_agent_stage.py 2>/dev/null","description":"Find stage validator requirements"},"output":"130: return receipt.get(\"search_or_fetch_calls\", 0) >= 2 and receipt.get(\"distinct_web_domains\", 0) >= 2\n/mnt/workdrive/research-stage/scripts/run_agent_stage.py\n/mnt/workdrive/rune/scripts/run_agent_stage.py\n","metadata":{"output":"130: return receipt.get(\"search_or_fetch_calls\", 0) >= 2 and receipt.get(\"distinct_web_domains\", 0) >= 2\n/mnt/workdrive/research-stage/scripts/run_agent_stage.py\n/mnt/workdrive/rune/scripts/run_agent_stage.py\n","exit":0,"description":"Find stage validator requirements","truncated":false},"title":"Find stage validator requirements","time":{"start":1788986260340,"end":1788986260342}},"id":"prt_g001a087e3f257001enbsxZzWB","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e3bea1001n36RUS4sJ"}} +{"type":"step_finish","timestamp":1788986260370,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e3fb77001FglzQS0Nl","reason":"tool-calls","snapshot":"75c18bfaa763aa78bfd1041dc46f0f28618ace4b","messageID":"msg_g001a087e3bea1001n36RUS4sJ","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":89034,"input":84,"output":374,"reasoning":0,"cache":{"write":0,"read":88576}},"cost":0}} +{"type":"step_start","timestamp":1788986270130,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e421b1001WsOCYDPz3","messageID":"msg_g001a087e3fbc0001r0GhjIEE3","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"ccd0897784c27b2385030055d941106687566228","type":"step-start"}} +{"type":"tool_use","timestamp":1788986270908,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_635ff319bbdf4e9d973d5d34","state":{"status":"completed","input":{"command":"grep -n \"OUTWARD\\|outward\" /mnt/workdrive/research-stage/scripts/run_agent_stage.py | head -20","description":"Find OUTWARD validation logic in executor"},"output":"20: a valid OUTWARD receipt: >=2 distinct fetched web domains);\n90: is deliberately invisible here. §10: no receipt -> OUTWARD INVALID.\n127:def outward_valid(receipt: dict) -> bool:\n128: \"\"\"§9: OUTWARD is valid only with real discovery — at least 2 distinct\n144: if not outward_valid(result.get(\"receipt\", {})):\n146: \"OUTWARD INVALID: receipt shows fewer than 2 completed search/fetch calls across 2 distinct web domains\"\n","metadata":{"output":"20: a valid OUTWARD receipt: >=2 distinct fetched web domains);\n90: is deliberately invisible here. §10: no receipt -> OUTWARD INVALID.\n127:def outward_valid(receipt: dict) -> bool:\n128: \"\"\"§9: OUTWARD is valid only with real discovery — at least 2 distinct\n144: if not outward_valid(result.get(\"receipt\", {})):\n146: \"OUTWARD INVALID: receipt shows fewer than 2 completed search/fetch calls across 2 distinct web domains\"\n","exit":0,"description":"Find OUTWARD validation logic in executor","truncated":false},"title":"Find OUTWARD validation logic in executor","time":{"start":1788986270904,"end":1788986270907}},"id":"prt_g001a087e421b2001MKQ5fQMrm","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e3fbc0001r0GhjIEE3"}} +{"type":"step_finish","timestamp":1788986270936,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e424bc001bKgj6m05E","reason":"tool-calls","snapshot":"26e30c00bf997ae3d121849c6964b091d2747973","messageID":"msg_g001a087e3fbc0001r0GhjIEE3","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":88902,"input":149,"output":49,"reasoning":0,"cache":{"write":0,"read":88704}},"cost":0}} +{"type":"step_start","timestamp":1788986284357,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e459430015qsKaF3vF","messageID":"msg_g001a087e425030012o0J0KUc6","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"c1c188e7d2f57f474027c9d5c8f35f4ff30b7828","type":"step-start"}} +{"type":"tool_use","timestamp":1788986284452,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"type":"tool","tool":"bash","callID":"call_542bcbb65dea4ffaab7f59aa","state":{"status":"completed","input":{"command":"sed -n 100,170p /mnt/workdrive/research-stage/scripts/run_agent_stage.py","description":"Read executor OUTWARD validation section"},"output":" continue\n tool = (part.get(\"tool\") or \"\").lower()\n state = part.get(\"state\") or {}\n status = state.get(\"status\")\n inp = state.get(\"input\") or {}\n if tool in WEB_TOOLS:\n if status == \"completed\":\n search_or_fetch += 1\n url = inp.get(\"url\") or \"\"\n host = urlparse(url).netloc if url else \"\"\n if host:\n domains.add(host)\n if \"q=\" in url or tool in {\"websearch\", \"firecrawl_search\", \"tavily_search\", \"search\"}:\n queries.append(url or json.dumps(inp)[:120])\n else:\n failed += 1\n elif tool in {\"read\", \"grep\", \"glob\"} and status == \"completed\":\n repo_reads += 1\n return {\n \"search_or_fetch_calls\": search_or_fetch,\n \"distinct_web_domains\": len(domains),\n \"failed_calls\": failed,\n \"repo_read_calls\": repo_reads,\n \"queries\": queries[:10],\n }\n\n\ndef outward_valid(receipt: dict) -> bool:\n \"\"\"§9: OUTWARD is valid only with real discovery — at least 2 distinct\n fetched web domains and at least 2 completed search/fetch calls.\"\"\"\n return receipt.get(\"search_or_fetch_calls\", 0) >= 2 and receipt.get(\"distinct_web_domains\", 0) >= 2\n\n\n# ------------------------------------------------------------------ schema\n\nREQUIRED_FIELDS = (\"issue\", \"stage\", \"round\", \"final_text\", \"receipt\", \"disposition\")\n\n\ndef validate_result(result: dict, stage: str | None = None) -> list[str]:\n errs = []\n for f in REQUIRED_FIELDS:\n if f not in result or result[f] in (None, \"\"):\n errs.append(f\"missing field: {f}\")\n if stage == \"research\" or result.get(\"stage\") == \"research\":\n if not outward_valid(result.get(\"receipt\", {})):\n errs.append(\n \"OUTWARD INVALID: receipt shows fewer than 2 completed search/fetch calls across 2 distinct web domains\"\n )\n if result.get(\"disposition\") not in (None, \"PROCEED\", \"MORE_RESEARCH\"):\n errs.append(f\"unknown disposition: {result.get('disposition')}\")\n return errs\n\n\n# ------------------------------------------------------------------ locks\n\n\ndef _lock_path(path: str) -> Path:\n p = Path(path)\n p.parent.mkdir(parents=True, exist_ok=True)\n return p\n\n\ndef acquire_lock(path: str, ttl_secs: int = LOCK_TTL_SECS) -> bool:\n p = _lock_path(path)\n if p.exists():\n age = time.time() - p.stat().st_mtime\n if age < ttl_secs:\n return False\n p.unlink() # stale lock from a crashed stage — take over\n p.write_text(f\"pid={os.getpid()} acquired={time.time():.0f}\")\n return True\n","metadata":{"output":" continue\n tool = (part.get(\"tool\") or \"\").lower()\n state = part.get(\"state\") or {}\n status = state.get(\"status\")\n inp = state.get(\"input\") or {}\n if tool in WEB_TOOLS:\n if status == \"completed\":\n search_or_fetch += 1\n url = inp.get(\"url\") or \"\"\n host = urlparse(url).netloc if url else \"\"\n if host:\n domains.add(host)\n if \"q=\" in url or tool in {\"websearch\", \"firecrawl_search\", \"tavily_search\", \"search\"}:\n queries.append(url or json.dumps(inp)[:120])\n else:\n failed += 1\n elif tool in {\"read\", \"grep\", \"glob\"} and status == \"completed\":\n repo_reads += 1\n return {\n \"search_or_fetch_calls\": search_or_fetch,\n \"distinct_web_domains\": len(domains),\n \"failed_calls\": failed,\n \"repo_read_calls\": repo_reads,\n \"queries\": queries[:10],\n }\n\n\ndef outward_valid(receipt: dict) -> bool:\n \"\"\"§9: OUTWARD is valid only with real discovery — at least 2 distinct\n fetched web domains and at least 2 completed search/fetch calls.\"\"\"\n return receipt.get(\"search_or_fetch_calls\", 0) >= 2 and receipt.get(\"distinct_web_domains\", 0) >= 2\n\n\n# ------------------------------------------------------------------ schema\n\nREQUIRED_FIELDS = (\"issue\", \"stage\", \"round\", \"final_text\", \"receipt\", \"disposition\")\n\n\ndef validate_result(result: dict, stage: str | None = None) -> list[str]:\n errs = []\n for f in REQUIRED_FIELDS:\n if f not in result or result[f] in (None, \"\"):\n errs.append(f\"missing field: {f}\")\n if stage == \"research\" or result.get(\"stage\") == \"research\":\n if not outward_valid(result.get(\"receipt\", {})):\n errs.append(\n \"OUTWARD INVALID: receipt shows fewer than 2 completed search/fetch calls across 2 distinct web domains\"\n )\n if result.get(\"disposition\") not in (None, \"PROCEED\", \"MORE_RESEARCH\"):\n errs.append(f\"unknown disposition: {result.get('disposition')}\")\n return errs\n\n\n# ------------------------------------------------------------------ locks\n\n\ndef _lock_path(path: str) -> Path:\n p = Path(path)\n p.parent.mkdir(parents=True, exist_ok=True)\n return p\n\n\ndef acquire_lock(path: str, ttl_secs: int = LOCK_TTL_SECS) -> bool:\n p = _lock_path(path)\n if p.exists():\n age = time.time() - p.stat().st_mtime\n if age < ttl_secs:\n return False\n p.unlink() # stale lock from a crashed stage — take over\n p.write_text(f\"pid={os.getpid()} acquired={time.time():.0f}\")\n return True\n","exit":0,"description":"Read executor OUTWARD validation section","truncated":false},"title":"Read executor OUTWARD validation section","time":{"start":1788986284448,"end":1788986284451}},"id":"prt_g001a087e45944001ZhHv2Rj7e","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","messageID":"msg_g001a087e425030012o0J0KUc6"}} +{"type":"step_finish","timestamp":1788986284480,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e459a4001YDc045TJj","reason":"tool-calls","snapshot":"4bb79d019dde78b9c1d2ecdcd155f74859e74d1b","messageID":"msg_g001a087e425030012o0J0KUc6","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":89055,"input":118,"output":41,"reasoning":0,"cache":{"write":0,"read":88896}},"cost":0}} +{"type":"step_start","timestamp":1788986300360,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e497c6001GxfWzuNU8","messageID":"msg_g001a087e459eb001hGyTVTL0r","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","snapshot":"fd54f4bf5167d19bd694baa1604ce1ca7450fc94","type":"step-start"}} +{"type":"text","timestamp":1788986313058,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e4998d001gF98gQJsT","messageID":"msg_g001a087e459eb001hGyTVTL0r","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"text","text":"Validation requirements confirmed: the executor derives the receipt from web-tool events in the session log — this session has 10 completed webfetch calls across 3 distinct domains (duckduckgo.com, raw.githubusercontent.com, archunit.org), comfortably above the ≥2/≥2 bar. Per pipeline convention (confirmed via issue-163/issue-310 history), the `task_tree.json` acceptance entry is written by the main agent/executor after it finalizes the artifact — I will not touch it.\n\n## OUTWARD stage complete — Issue #311 (ADAPT-1 B2, wiring-status/architecture-truth report generator)\n\n**Artifact**: `/mnt/workdrive/issue161-harness/research/issue-311/outward.json` (valid JSON: 6 queries, 10 sources, 8 evidence claims)\n\n### What was actually done\n- **Skill search** performed (first-query requirement; firecrawl-search/super-research surfaced but not loaded — plain webfetch matches the established technique from prior rounds).\n- **Discovery — 6 real DuckDuckGo SERP fetches** (1 returned zero results and is recorded as such).\n- **Reading — 5 full reads**: import-linter README (after a 404 on `.rst`, retried `.md`), dependency-cruiser README, vulture README, ArchUnit 1.5.0 user guide (truncated mid-page — recorded as limitation), plus 4 repo-side receipts (R1–R4).\n\n### Key evidence claims\n- **O1/O4/O6 (converged industry pattern)**: machine-generated architecture truth comes from the import graph + declarative contracts/rules; dependency-cruiser's shape is most relevant — one engine emits both a validation report and a human status/graph view, and dogfoods itself in its own build.\n- **O5 (vulture)**: liveness claims (\"not-live / not-causal\") from static analysis are inherently probabilistic — confidence levels + whitelists needed because registry/hook/dynamic-dispatch wiring reads as dead. Directly relevant to #158's acceptance criterion of flagging a deliberately disconnected subsystem.\n- **E5**: `config/v1_authority_map.json` exists as the issue claims, already machine-readable (dispositions, responsibilities) — usable directly as the authority-coverage source.\n- **E6**: `gen_prd_state.py` is the in-house precedent for generated-truth reports with the hand-written vs generated split.\n\n### Contradiction found\n- **E8**: issue #311 names \"module scan (walkman pattern exists)\" as a dependency — repo-wide grep finds **zero** \"walkman\" hits anywhere. The issue's dependency list is stale; later stages must not lean on it.\n\n### Recorded as NOT done\nNo synthesis, no design, no code modifications, no further research beyond the evidence bar. Per stage contract, the artifact does not carry the `StageCompleted:` marker inline (verified against prior accepted JSON artifacts); the marker appears here as required by the stage prompt:\n\nStageCompleted: OUTWARD","time":{"start":1788986300813,"end":1788986313056}}} +{"type":"step_finish","timestamp":1788986313082,"sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","part":{"id":"prt_g001a087e4c962001OXdzrjnOc","reason":"stop","snapshot":"98679976e40e446e96752fefc7d657529fd557f8","messageID":"msg_g001a087e459eb001hGyTVTL0r","sessionID":"ses_-ffe5f782475a4ffegE8Ix6aZJ","type":"step-finish","tokens":{"total":90605,"input":656,"output":925,"reasoning":0,"cache":{"write":0,"read":89024}},"cost":0}} diff --git a/research/issue-311/task_tree.json b/research/issue-311/task_tree.json new file mode 100644 index 00000000..e96015a8 --- /dev/null +++ b/research/issue-311/task_tree.json @@ -0,0 +1,237 @@ +{ + "issue": 311, + "round": 1, + "current_stage": "OUTWARD", + "completed_stages": [ + "ISSUE", + "INWARD" + ], + "artifacts": { + "ISSUE": { + "contract": { + "issue": 311, + "title": "ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)", + "question_type": "mixed", + "sub_questions": [ + "Address: ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)" + ], + "required_stages": [ + "ISSUE", + "INWARD", + "OUTWARD", + "ADVERSARIAL", + "INNOVATION", + "SYNTHESIS", + "QUALIFY" + ], + "skipped_stages": { + "APPLICABILITY": "No OUTWARD research to ground against Rune." + }, + "evidence_classes": [ + "repo", + "external" + ], + "sufficient_evidence": "\u22652 code-path citations for INWARD; \u22652 unique URLs for OUTWARD (if required)", + "expected_artifact": "synthesis.json", + "ambiguous": false, + "needs_mimo": false, + "source": "deterministic" + }, + "deterministic": true + }, + "INWARD": { + "context": { + "keywords": [ + "adapt", + "standard", + "like", + "wiring", + "status", + "architecture", + "truth", + "report", + "generator", + "pre", + "registered", + "adapt", + "baseline", + "task", + "implements", + "open", + "issue", + "baseline", + "master", + "expected" + ], + "umbrella_matches": [ + { + "umbrella": "memory", + "module": "drift_compression", + "keyword": "pre" + }, + { + "umbrella": "perception", + "module": "context_pressure", + "keyword": "pre" + }, + { + "umbrella": "perception", + "module": "auto_tasks", + "keyword": "task" + }, + { + "umbrella": "inference", + "module": "standardized_providers", + "keyword": "standard" + }, + { + "umbrella": "inference", + "module": "response_adapter", + "keyword": "adapt" + }, + { + "umbrella": "orchestration", + "module": "task_gate", + "keyword": "task" + }, + { + "umbrella": "orchestration", + "module": "task_persist", + "keyword": "task" + } + ], + "candidate_files": [ + "src/rune/rune_operator.py", + "src/rune/elastic_cognition.py", + "src/rune/rune_tui.py", + "src/rune/contracts.py", + "src/rune/memory_store.py", + "src/rune/claim_audit.py", + "src/rune/curator.py", + "src/rune/growth_delta.py", + "src/rune/safety_override.py", + "src/rune/voice_hybrid.py", + "src/rune/canary_full_system.py", + "src/rune/canary_native.py", + "src/rune/capability_broker.py", + "src/rune/failure_auto_filer.py", + "src/rune/hook_engine.py" + ], + "candidate_tests": [ + "tests/test_pipeline_integration.py", + "tests/test_research_task_tree.py", + "tests/test_adapt9_artifact_authority.py", + "tests/test_failure_auto_filer_task2.py", + "tests/test_merge_robot.py", + "tests/test_p1_01_capability_authority.py", + "tests/test_p1_06_model_routing.py", + "tests/test_synthesis_executor.py", + "tests/test_applicability_executor.py", + "tests/test_p0_07_inference_trace.py" + ], + "define_contract": { + "issue": 311, + "title": "ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)", + "question_type": "mixed", + "sub_questions": [ + "Address: ADAPT-1 B2 (STANDARD-LIKE): wiring-status/architecture-truth report generator (#158)" + ], + "required_stages": [ + "ISSUE", + "INWARD", + "OUTWARD", + "ADVERSARIAL", + "INNOVATION", + "SYNTHESIS", + "QUALIFY" + ], + "skipped_stages": { + "APPLICABILITY": "No OUTWARD research to ground against Rune." + }, + "evidence_classes": [ + "repo", + "external" + ], + "sufficient_evidence": "\u22652 code-path citations for INWARD; \u22652 unique URLs for OUTWARD (if required)", + "expected_artifact": "synthesis.json", + "ambiguous": false, + "needs_mimo": false, + "source": "deterministic" + }, + "mimo_needed": false + }, + "deterministic": true, + "claims": [ + { + "id": "I1", + "claim": "Relevant file: src/rune/rune_operator.py", + "evidence": [ + "src/rune/rune_operator.py" + ], + "status": "CURRENT" + }, + { + "id": "I2", + "claim": "Relevant file: src/rune/elastic_cognition.py", + "evidence": [ + "src/rune/elastic_cognition.py" + ], + "status": "CURRENT" + }, + { + "id": "I3", + "claim": "Relevant file: src/rune/rune_tui.py", + "evidence": [ + "src/rune/rune_tui.py" + ], + "status": "CURRENT" + }, + { + "id": "I4", + "claim": "Relevant file: src/rune/contracts.py", + "evidence": [ + "src/rune/contracts.py" + ], + "status": "CURRENT" + }, + { + "id": "I5", + "claim": "Relevant file: src/rune/memory_store.py", + "evidence": [ + "src/rune/memory_store.py" + ], + "status": "CURRENT" + } + ] + } + }, + "blocking_unknowns": [], + "status": "ACTIVE", + "stage_history": [ + { + "stage": "ISSUE", + "round": 1, + "completed_at": "auto", + "artifact_keys": [ + "contract", + "deterministic" + ] + }, + { + "stage": "INWARD", + "round": 1, + "completed_at": "auto", + "artifact_keys": [ + "context", + "deterministic", + "claims" + ] + }, + { + "stage": "OUTWARD", + "round": 1, + "failed": true, + "reason": "OUTWARD: 0 sources < 2; OUTWARD: no sources with real read receipts \u2014 search snippets alone do not count as evidence" + } + ] +} \ No newline at end of file diff --git a/research/issue-311/telemetry/attempts.jsonl b/research/issue-311/telemetry/attempts.jsonl new file mode 100644 index 00000000..feced4b8 --- /dev/null +++ b/research/issue-311/telemetry/attempts.jsonl @@ -0,0 +1,3 @@ +{"attempt_id": "issue-311-ISSUE-1-a1", "event_id": "issue-311-ISSUE-1", "stage": "ISSUE", "issue": 311, "process_id": "research-311-r1", "started_at": "2026-09-09T20:28:04.608125+00:00", "finished_at": "2026-09-09T20:28:04.796758+00:00", "terminal_status": "COMPLETED", "wall_time_s": 0.1887, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-311-INWARD-1-a1", "event_id": "issue-311-INWARD-1", "stage": "INWARD", "issue": 311, "process_id": "research-311-r1", "started_at": "2026-09-09T20:28:04.797445+00:00", "finished_at": "2026-09-09T20:28:04.934584+00:00", "terminal_status": "COMPLETED", "wall_time_s": 0.1372, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} +{"attempt_id": "issue-311-OUTWARD-1-a1", "event_id": "issue-311-OUTWARD-1", "stage": "OUTWARD", "issue": 311, "process_id": "research-311-r1", "started_at": "2026-09-09T20:28:25.765268+00:00", "finished_at": "2026-09-09T20:40:38.899495+00:00", "terminal_status": "GATE_FAILED", "wall_time_s": 733.1343, "model_time_s": 0, "tool_time_s": 0, "gate_time_s": 0, "persistence_time_s": 0, "overlap_note": "categories are observed windows and may overlap; wall_time is authoritative, category sums are not additive", "model_calls": 0, "tool_calls": 0, "repo_read_calls": 0, "external_queries": 0, "sources_discovered": 0, "sources_read": 0, "retries": 0, "reconciliation_actions": 0, "implementation_corrections": 0, "ci_failures": 0, "input_tokens": "UNKNOWN_NOT_EXPOSED", "output_tokens": "UNKNOWN_NOT_EXPOSED", "queue_wait_s": null, "window_counts": {"model": 0, "tool": 0, "gate": 0, "persistence": 0}} diff --git a/scripts/research_event_relay.py b/scripts/research_event_relay.py index 3063d8a7..a8207d29 100644 --- a/scripts/research_event_relay.py +++ b/scripts/research_event_relay.py @@ -21,11 +21,15 @@ from typing import Callable class ResearchEventRelay: """Reads mimo JSON events in real-time and updates canonical state.""" - def __init__(self, tree, events_path: str, mimo_cmd: list[str], err_path: str): + def __init__(self, tree, events_path: str, mimo_cmd: list[str], err_path: str, + attempt=None): self.tree = tree self.events_path = events_path self.mimo_cmd = mimo_cmd self.err_path = err_path + # OBS-1/D3 (CORRECTION-2): observational telemetry handle. The relay + # marks tool windows; it never changes classification or control flow. + self.attempt = attempt self.event_counts = { "queries": 0, "sources_discovered": 0, "sources_read": 0, "evidence_accepted": 0, "tool_failures": 0, "tool_calls": 0, @@ -47,6 +51,11 @@ class ResearchEventRelay: status = state.get("status", "") tool_input = state.get("input", {}) + if self.attempt is not None and status == "running": + self.attempt.mark_tool_start() + if self.attempt is not None and status in ("completed", "error", "failed"): + self.attempt.mark_tool_end() + if status == "completed": self.event_counts["tool_calls"] += 1 if "url" in tool_input: diff --git a/scripts/research_stage_executor.py b/scripts/research_stage_executor.py index 4d4405d1..f916f140 100644 --- a/scripts/research_stage_executor.py +++ b/scripts/research_stage_executor.py @@ -26,6 +26,7 @@ The executor: from __future__ import annotations import hashlib +import re import json import os import sys @@ -91,7 +92,7 @@ class StageExecutor: _ledger = TelemetryLedger(self.artifact_dir) # Execute ONLY this stage - result = self._execute_stage(stage, attempt, event) + result = self._execute_stage(stage, attempt, event, telemetry_attempt=_attempt) _attempt.finish( "COMPLETED" if result.get("status") == "STAGE_COMPLETED" @@ -127,28 +128,29 @@ class StageExecutor: return result - def _execute_stage(self, stage: str, attempt: int, event: dict) -> dict: + def _execute_stage(self, stage: str, attempt: int, event: dict, + telemetry_attempt=None) -> dict: """Execute one stage. Returns the stage result. Canonical stages (RP2): ISSUE (deterministic gate) replaces DEFINE.""" if stage == "ISSUE": - return self._execute_issue_gate(event, attempt) + return self._execute_issue_gate(event, attempt, telemetry_attempt=telemetry_attempt) elif stage in ("DEFINE", "OUTWARD_DISCOVERY", "OUTWARD_READING"): # Retired stages: cannot be executed, cannot become READY (§26.5) return self._execute_retired(stage) elif stage == "INWARD": - return self._execute_inward(event, attempt) + return self._execute_inward(event, attempt, telemetry_attempt=telemetry_attempt) elif stage == "OUTWARD": - return self._execute_outward(event) + return self._execute_outward(event, telemetry_attempt=telemetry_attempt) elif stage == "APPLICABILITY": - return self._execute_applicability(event) + return self._execute_applicability(event, telemetry_attempt=telemetry_attempt) elif stage == "ADVERSARIAL": - return self._execute_adversarial(event) + return self._execute_adversarial(event, telemetry_attempt=telemetry_attempt) elif stage == "INNOVATION": - return self._execute_innovation(event) + return self._execute_innovation(event, telemetry_attempt=telemetry_attempt) elif stage == "SYNTHESIS": - return self._execute_synthesis(event) + return self._execute_synthesis(event, telemetry_attempt=telemetry_attempt) elif stage == "QUALIFY": - return self._execute_qualify(event) + return self._execute_qualify(event, telemetry_attempt=telemetry_attempt) else: return self._execute_not_implemented(stage) @@ -167,7 +169,7 @@ class StageExecutor: ), } - def _execute_issue_gate(self, event: dict, attempt: int = 1) -> dict: + def _execute_issue_gate(self, event: dict, attempt: int = 1, telemetry_attempt=None) -> dict: """ISSUE is the deterministic issue-quality gate (replaces DEFINE). The Gitea issue IS the definition of intent; this only checks it is buildable — no cognitive stage, no model call.""" @@ -205,7 +207,7 @@ class StageExecutor: "artifact": artifact, } - def _execute_inward(self, event: dict, attempt: int = 1) -> dict: + def _execute_inward(self, event: dict, attempt: int = 1, telemetry_attempt=None) -> dict: """INWARD: deterministic context builder + optional bounded MiMo.""" from research_inward_context import build_context from research_define import compile_define_contract @@ -259,6 +261,28 @@ class StageExecutor: "artifact": artifact, } + @staticmethod + def _load_worker_artifact_file(path: str) -> dict: + """CORRECTION-1 (D2): read the worker artifact file. + + CANONICAL contract: the file contains exactly one JSON document. + LEGACY_COMPATIBILITY_ONLY: some historical workers append the literal + sentinel 'StageCompleted: ' after the JSON. The reader permits + ONLY that exact recognized sentinel — no arbitrary trailing-text + salvage. New workers must not append it (prompts updated).""" + with open(path) as f: + raw = f.read() + try: + return json.loads(raw) + except json.JSONDecodeError: + pass + # LEGACY_COMPATIBILITY_ONLY — exactly one known sentinel pattern + m = re.match(r"^(\{.*\})\s*\n?StageCompleted: [A-Z_]+\s*$", raw, re.DOTALL) + if m: + return json.loads(m.group(1)) + raise json.JSONDecodeError("artifact file is not a single JSON document", + raw, 0) + def _resolve_worker_artifact(self, stage: str, relay_result: dict) -> tuple[dict | None, str]: """ADAPT-9/#298 artifact authority. @@ -276,8 +300,7 @@ class StageExecutor: self.artifact_dir, STAGE_ARTIFACT_FILES.get(stage, f"{stage.lower()}.json")) if os.path.exists(artifact_file): try: - with open(artifact_file) as f: - worker_artifact = json.load(f) + worker_artifact = self._load_worker_artifact_file(artifact_file) if isinstance(worker_artifact, dict) and worker_artifact: return worker_artifact, "file" except (json.JSONDecodeError, OSError): @@ -300,7 +323,19 @@ class StageExecutor: pass return None, "none" - def _execute_outward(self, event: dict) -> dict: + def _run_relay_with_telemetry(self, events_path, cmd, err_path, attempt): + """CORRECTION-2 (D3): run the relay with model windows marked around it + and tool windows marked inside it. Observational only.""" + from research_event_relay import ResearchEventRelay + relay = ResearchEventRelay(self.tree, events_path, cmd, err_path, attempt=attempt) + attempt.mark_model_start() + try: + result = relay.run() + finally: + attempt.mark_model_end() + return result + + def _execute_outward(self, event: dict, telemetry_attempt=None) -> dict: """OUTWARD: one coherent MiMo external-research pass (discovery + reading + evidence extraction).""" import re as _re @@ -360,9 +395,7 @@ class StageExecutor: cmd = [mimo_bin, "run", "--agent", mimo_agent, "-m", mimo_model, "--dangerously-skip-permissions", "--format", "json", prompt] - from research_event_relay import ResearchEventRelay - relay = ResearchEventRelay(self.tree, events_path, cmd, err_path) - relay_result = relay.run() + relay_result = self._run_relay_with_telemetry(events_path, cmd, err_path, telemetry_attempt) if relay_result["returncode"] != 0: self.tree.fail("mimo exited " + str(relay_result["returncode"])) @@ -378,8 +411,7 @@ class StageExecutor: self.artifact_dir, STAGE_ARTIFACT_FILES.get("OUTWARD", "outward.json")) if os.path.exists(artifact_file): try: - with open(artifact_file) as f: - worker_artifact = json.load(f) + worker_artifact = self._load_worker_artifact_file(artifact_file) except (json.JSONDecodeError, OSError) as e: error_detail = f"unreadable artifact file: {e}" self.tree.fail("no text output. " + error_detail) @@ -525,7 +557,7 @@ class StageExecutor: return {"status": "STAGE_COMPLETED", "stage": "OUTWARD", "artifact_hash": artifact_digest, "next_stage": next_stage, "artifact": artifact} - def _execute_applicability(self, event: dict) -> dict: + def _execute_applicability(self, event: dict, telemetry_attempt=None) -> dict: """APPLICABILITY (RP4/#267): classify OUTWARD mechanisms against Rune's actual architecture, decide the INNOVATION route. One bounded MiMo pass; the structured dossier is the authority (file channel accepted).""" @@ -582,9 +614,7 @@ class StageExecutor: cmd = [mimo_bin, "run", "--agent", mimo_agent, "-m", mimo_model, "--dangerously-skip-permissions", "--format", "json", prompt] - from research_event_relay import ResearchEventRelay - relay = ResearchEventRelay(self.tree, events_path, cmd, err_path) - relay_result = relay.run() + relay_result = self._run_relay_with_telemetry(events_path, cmd, err_path, telemetry_attempt) if relay_result["returncode"] != 0: self.tree.fail("mimo exited " + str(relay_result["returncode"])) @@ -611,8 +641,7 @@ class StageExecutor: artifact_file = os.path.join(self.artifact_dir, "applicability.json") if os.path.exists(artifact_file): try: - with open(artifact_file) as f: - file_artifact = json.load(f) + file_artifact = self._load_worker_artifact_file(artifact_file) if file_artifact.get("candidates"): worker_artifact = file_artifact except (json.JSONDecodeError, OSError): @@ -688,7 +717,7 @@ class StageExecutor: errors.append(f"candidate {c.get('id', '?')}: cites unknown source {sid}") return errors - def _execute_adversarial(self, event: dict) -> dict: + def _execute_adversarial(self, event: dict, telemetry_attempt=None) -> dict: """ADVERSARIAL (RP6/#269): attack the leading candidate(s) from APPLICABILITY. One bounded MiMo pass with targeted external checking permitted. The structured dossier is the authority.""" @@ -752,9 +781,7 @@ class StageExecutor: cmd = [mimo_bin, "run", "--agent", mimo_agent, "-m", mimo_model, "--dangerously-skip-permissions", "--format", "json", prompt] - from research_event_relay import ResearchEventRelay - relay = ResearchEventRelay(self.tree, events_path, cmd, err_path) - relay_result = relay.run() + relay_result = self._run_relay_with_telemetry(events_path, cmd, err_path, telemetry_attempt) if relay_result["returncode"] != 0: self.tree.fail("mimo exited " + str(relay_result["returncode"])) @@ -857,7 +884,7 @@ class StageExecutor: f"{a.get('target_candidate')} without rebuttal") return errors - def _execute_innovation(self, event: dict) -> dict: + def _execute_innovation(self, event: dict, telemetry_attempt=None) -> dict: """INNOVATION (RP5/#268): conditional cross-domain/compositional stage. Runs only when APPLICABILITY routed INNOVATION_WARRANTED. Valid success outcomes include NO_INNOVATION_WARRANTED — never manufacture novelty.""" @@ -914,9 +941,7 @@ class StageExecutor: cmd = [mimo_bin, "run", "--agent", mimo_agent, "-m", mimo_model, "--dangerously-skip-permissions", "--format", "json", prompt] - from research_event_relay import ResearchEventRelay - relay = ResearchEventRelay(self.tree, events_path, cmd, err_path) - relay_result = relay.run() + relay_result = self._run_relay_with_telemetry(events_path, cmd, err_path, telemetry_attempt) if relay_result["returncode"] != 0: self.tree.fail("mimo exited " + str(relay_result["returncode"])) @@ -987,7 +1012,7 @@ class StageExecutor: errors.append(f"candidate {c.get('id', '?')}: invalid category {c.get('category')!r}") return errors - def _execute_synthesis(self, event: dict) -> dict: + def _execute_synthesis(self, event: dict, telemetry_attempt=None) -> dict: """SYNTHESIS (RP7/#270): turn ONLY adversarially surviving material into concrete build instructions. Inputs are already epistemically settled — no new research, no candidate resurrection.""" @@ -1069,9 +1094,7 @@ class StageExecutor: cmd = [mimo_bin, "run", "--agent", mimo_agent, "-m", mimo_model, "--dangerously-skip-permissions", "--format", "json", prompt] - from research_event_relay import ResearchEventRelay - relay = ResearchEventRelay(self.tree, events_path, cmd, err_path) - relay_result = relay.run() + relay_result = self._run_relay_with_telemetry(events_path, cmd, err_path, telemetry_attempt) if relay_result["returncode"] != 0: self.tree.fail("mimo exited " + str(relay_result["returncode"])) @@ -1107,7 +1130,7 @@ class StageExecutor: "artifact_hash": artifact_digest, "next_stage": next_stage, "artifact": artifact} - def _execute_qualify(self, event: dict) -> dict: + def _execute_qualify(self, event: dict, telemetry_attempt=None) -> dict: """QUALIFY (RP8/#271): implementation-readiness gate, narrowed semantics. Deterministic-first. The core question: is the accepted synthesis diff --git a/tests/test_adapt9_artifact_authority.py b/tests/test_adapt9_artifact_authority.py index 6663581e..f1bc19d7 100644 --- a/tests/test_adapt9_artifact_authority.py +++ b/tests/test_adapt9_artifact_authority.py @@ -225,3 +225,48 @@ def test_retry_does_not_overwrite_history(tree, mock_gitea): "attempt": 2, "event_id": "hist-2"}) fails_after = len([h for h in tree.state["stage_history"] if h.get("failed")]) assert fails_after >= fails_before, "failed history must not be erased" + + +# ── CORRECTION-1 (D2): producer contract + sentinel-tolerant legacy reader ─── + +def test_reader_accepts_pure_json(tree): + ex = rse.StageExecutor(issue=tree.issue, artifact_dir=tree.artifact_dir, round_no=1) + p = Path(tree.artifact_dir) / "adversarial.json" + p.write_text(json.dumps({"surviving_candidates": ["M1"]})) + assert ex._load_worker_artifact_file(str(p)) == {"surviving_candidates": ["M1"]} + + +def test_reader_accepts_exact_legacy_sentinel(tree): + """LEGACY_COMPATIBILITY_ONLY: exactly one known sentinel tolerated.""" + ex = rse.StageExecutor(issue=tree.issue, artifact_dir=tree.artifact_dir, round_no=1) + p = Path(tree.artifact_dir) / "adversarial.json" + p.write_text(json.dumps({"surviving_candidates": ["M1"]}) + "\nStageCompleted: ADVERSARIAL\n") + assert ex._load_worker_artifact_file(str(p)) == {"surviving_candidates": ["M1"]} + + +def test_reader_rejects_arbitrary_trailing_text(tree): + """No salvage: arbitrary trailing text (not the known sentinel) → JSONDecodeError.""" + ex = rse.StageExecutor(issue=tree.issue, artifact_dir=tree.artifact_dir, round_no=1) + p = Path(tree.artifact_dir) / "adversarial.json" + p.write_text(json.dumps({"surviving_candidates": ["M1"]}) + "\nsome worker chatter here\n") + with pytest.raises(json.JSONDecodeError): + ex._load_worker_artifact_file(str(p)) + + +def test_reader_rejects_corrupt_json(tree): + """Actual corruption is never masked as salvage.""" + ex = rse.StageExecutor(issue=tree.issue, artifact_dir=tree.artifact_dir, round_no=1) + p = Path(tree.artifact_dir) / "adversarial.json" + p.write_text('{"surviving_candidates": ["M1"], truncated...') + with pytest.raises(json.JSONDecodeError): + ex._load_worker_artifact_file(str(p)) + + +def test_prompts_require_pure_json_artifact_file(): + """Producer contract: every stage template states the artifact file must be + exactly one JSON document (markers to stdout only).""" + for f in (ROOT / "prompts" / "research-stages").glob("*.md"): + content = f.read_text() + if "StageCompleted:" in content: + assert "artifact file must contain exactly one JSON document" in content, \ + f"{f.name}: producer contract missing"