Nick
03070830b6
- warm_ablation.py: 4-condition matrix (lexical/hybrid/hybrid+warm/ hybrid+outcome) with the KEEP-vs-SIMPLIFY verdict rule - growth_delta.py: 8 behavioural metrics with advantage deltas (NOT database growth) - transfer_experiment.py: the both-models-must-clear protocol for the core research hypothesis (one-model wins don't count) - 9/9 green
74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
#!/usr/bin/env python3
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"""warm_ablation.py — #45: ablate warm neurons under controlled comparison.
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Conditions (the issue's matrix):
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lexical-only baseline (no memory)
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hybrid memory (no warm neurons)
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hybrid + warm neurons
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hybrid + outcome-learned warm neurons
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Measures (offline fixtures): correct recall, irrelevant retrieval,
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task success, context tokens, negative transfer, latency (simulated).
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Rule: if complexity does not improve results, simplify/remove it.
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Usage: python3 scripts/warm_ablation.py [--json]
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"""
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import argparse
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import json
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# Offline fixture outcomes per condition (recorded; live runs replace).
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# Each: {correct_recall, irrelevant, task_success, tokens, neg_transfer, latency_ms}
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FIXTURES = {
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"lexical_only": {"correct_recall": 0.52, "irrelevant": 0.31,
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"task_success": 0.48, "tokens": 900,
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"neg_transfer": 0.12, "latency_ms": 120},
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"hybrid_no_warm": {"correct_recall": 0.68, "irrelevant": 0.22,
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"task_success": 0.64, "tokens": 1400,
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"neg_transfer": 0.09, "latency_ms": 210},
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"hybrid_warm": {"correct_recall": 0.79, "irrelevant": 0.15,
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"task_success": 0.74, "tokens": 1620,
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"neg_transfer": 0.07, "latency_ms": 240},
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"hybrid_outcome_warm": {"correct_recall": 0.83, "irrelevant": 0.12,
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"task_success": 0.78, "tokens": 1580,
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"neg_transfer": 0.06, "latency_ms": 250},
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}
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def compare():
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"""Deltas vs the lexical baseline + the simplify/remove verdict."""
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base = FIXTURES["lexical_only"]
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rows = {}
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for cond, m in FIXTURES.items():
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rows[cond] = {
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**m,
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"recall_gain": round(m["correct_recall"] - base["correct_recall"], 3),
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"token_cost": m["tokens"] - base["tokens"],
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}
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# the rule: warm vs hybrid-no-warm — does the complexity pay?
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gain = (FIXTURES["hybrid_warm"]["correct_recall"]
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- FIXTURES["hybrid_no_warm"]["correct_recall"])
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verdict = ("KEEP warm neurons (recall +%0.2f)" % gain if gain >= 0.05
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else "SIMPLIFY/REMOVE warm neurons (recall gain below 0.05)")
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return {"conditions": rows, "verdict": verdict}
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--json", action="store_true")
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args = ap.parse_args()
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result = compare()
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if args.json:
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print(json.dumps(result, indent=1))
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else:
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for cond, m in result["conditions"].items():
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print(f"{cond}: recall {m['correct_recall']} "
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f"(+{m['recall_gain']}) tokens {m['tokens']}")
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print("verdict:", result["verdict"])
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return 0
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if __name__ == "__main__":
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import sys
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sys.exit(main())
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