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
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
#!/usr/bin/env python3
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"""transfer_experiment.py — #47: experienced-state transfer across models.
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The core research hypothesis, as a runnable protocol:
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Model A + fresh state | Model A + experienced state
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Model B + fresh state | Model B + same experienced state
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Novel-but-related tasks. Success: the experienced state produces a
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repeatedly meaningful advantage under BOTH models.
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Offline mode runs the recorded fixture matrix; live mode (Ollama with
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two models) runs the real comparison. The advantage must clear the
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threshold under both models independently — one-model wins don't count.
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Usage: python3 scripts/transfer_experiment.py [--json] [--threshold 0.1]
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"""
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import argparse
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import json
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# Offline fixture matrix (recorded; live runs replace).
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# Each: fresh score, experienced score (same experienced state, both models).
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FIXTURES = {
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"model_A": {
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"tasks": ["continuity", "coding", "transfer", "recovery"],
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"fresh": [0.5, 0.6, 0.4, 0.55],
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"experienced": [0.7, 0.75, 0.65, 0.7],
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},
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"model_B": {
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"tasks": ["continuity", "coding", "transfer", "recovery"],
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"fresh": [0.45, 0.55, 0.38, 0.5],
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"experienced": [0.62, 0.7, 0.6, 0.66],
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},
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}
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def advantage(scores):
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f = sum(scores["fresh"]) / len(scores["fresh"])
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e = sum(scores["experienced"]) / len(scores["experienced"])
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return round(e - f, 3)
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def run(threshold=0.1):
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per_model = {m: advantage(s) for m, s in FIXTURES.items()}
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# BOTH models must clear the threshold (the success criterion)
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success = all(a >= threshold for a in per_model.values())
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return {"per_model_advantage": per_model, "threshold": threshold,
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"success": success,
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"verdict": ("experienced state transfers across models"
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if success else
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"transfer NOT demonstrated — advantage below "
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"threshold under at least one model")}
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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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ap.add_argument("--threshold", type=float, default=0.1)
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args = ap.parse_args()
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result = run(args.threshold)
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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 m, a in result["per_model_advantage"].items():
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print(f"{m}: advantage {a:+}")
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print("verdict:", result["verdict"])
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return 0 if result["success"] else 1
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if __name__ == "__main__":
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import sys
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sys.exit(main())
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