Эксперимент: Role-Filler Attention (#1133)

{ "worked": true, "confidence": 8, "verdict": "Built a deterministic NumPy MVP for role-filler tensor construction, role contraction, soft retrieval, target-role extraction, and rebinding. Orthonormal contraction error was 1.33e-15; exact queries selected the correct object with 0.995 accuracy across 2,000 episodes, versus 0.579 for the flattened dense control, and rebinding error was 0.035 versus 2.38. Exact-query entropy was consistently lower than mismatched-role entropy, although this is a toy retrieval result rather than evidence for end-to-end neural generalization.", "metrics": { "baseline": "Flattened dense dot-product control: accuracy 0.579 (repeat 0.551), rebinding L2 error 2.383 (repeat 2.553), entropy 0.069 (repeat 0.073).", "idea": "Role-filler retrieval: accuracy 0.995 (repeat 0.995), rebinding L2 error 0.0348 (repeat 0.0434), entropy 0.00555 (mismatched-role 0.2085; repeat 0.00983 vs 0.1806). Role contraction maximum absolute error: 1.33e-15." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 role_filler_experiment.py", "files": [ "role_filler_experiment.py", "results.json" ], "limitations": "The experiment is a small NumPy episodic retrieval benchmark, not a trained encoder/Transformer integration. The dense comparison is a simple flattened dot-product control rather than a parameter-matched learned self-attention model; no held-out colored-shape dataset, FLOP matching, GPU timing, or large-scale generalization test was performed." }