Стенд-проверка (stage-2) · промт оператора:

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented an end-to-end Role-Filler Attention replacement on the structurally matched sequence forecasting track, with identical embedding, positional, feed-forward, and output components for baseline and idea systems. Across 8 paired seeds, the idea had MSE 0.563105 versus baseline 0.562902, paired delta +0.000203 and permutation p=0.9927, so there was no improvement. The trained-model mechanism signature also failed quantitatively: the observed exact-versus-mismatched role-mass gap was only 0.00263 versus the predicted 0.2.", "metrics": { "baseline": "Best dense-attention sweep config lr=0.001, tau=2.0; full 8-seed MSE mean 0.562902, std 0.061053.", "idea": "Best role-filler config lr=0.001, tau=1.0; full 8-seed MSE mean 0.563105, std 0.058728; paired delta +0.000203; permutation p=0.9927; 3/8 paired wins." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 role_filler_bench.py", "files": [ "role_filler_bench.py", "bench_report.json" ], "limitations": "Only the built-in sequence forecasting track was tested; no colored-shape held-out-factor benchmark, FLOP/timing study, vision track, or larger-scale training was run. The MVP uses position-indexed role vectors and soft role-conditioned retrieval rather than a full learned role/filler tensor-product query. The mechanism signature measures trained-model role probability mass rather than entropy.", "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001, "tau": 2.0 }, "full": { "mean": 0.5629024468362331, "std": 0.0610525769421287, "per_seed": [ 0.639130711555481, 0.47835078835487366, 0.5805663466453552, 0.5667509436607361, 0.6444448828697205, 0.4818141460418701, 0.5961759686470032, 0.5159857869148254 ], "n": 8 }, "sweep_size": 9 }, "idea": { "best_cfg": { "lr": 0.001, "tau": 1.0 }, "mean": 0.5631052926182747, "std": 0.05872795158375719, "per_seed": [ 0.6507832407951355, 0.4800701141357422, 0.6299005746841431, 0.5685753226280212, 0.5922347903251648, 0.5042065382003784, 0.5788382887840271, 0.5002334713935852 ], "n": 8 }, "comparison": { "delta_mean": 0.00020284578204154968, "idea_wins": 3, "n_pairs": 8, "per_seed_diffs": [ 0.011652529239654541, 0.0017193257808685303, 0.04933422803878784, 0.0018243789672851562, -0.052210092544555664, 0.0223923921585083, -0.017337679862976074, -0.015752315521240234 ], "p_value": 0.9927, "mde": 0.025187222915370296, "mde_rel_pct": 4.47452716841682, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "role-selective exact queries should be more concentrated than mismatched queries", "predicted_exact_minus_mismatched_mass": 0.2, "observed_exact_role_mass": 0.03502586856484413, "observed_mismatched_role_mass": 0.03239620546810329, "observed_gap": 0.002629663096740842, "confirmed": false } }, "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }