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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Built a local trace-canonical modular sequence model and evaluated it against the identical noncanonical baseline on the structurally matched sequence forecast track. The mechanism signature was confirmed quantitatively: observed maximum commuting-swap error was 0.0, while the median noncommuting difference was 1.85068. However, baseline and idea had exactly identical test MSE (0.1316999), paired delta 0.0, and permutation p-value 1.0; canonicalization changes representation/cache keys, not the trained function or learning outcome here.", "metrics": { "baseline": "sequence / modular_sequence_local; best lr=0.003; full 8-seed test MSE mean=0.1316999011, std=0.0170872853", "idea": "best lr=0.003; full 8-seed test MSE mean=0.1316999011, std=0.0170872853; paired delta=0.0; permutation p=1.0; observed commuting max error=0.0; observed noncommuting median difference=1.85068297" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "This benchmark tests task-level training rather than a full architecture-search wall-clock/cache evaluation; it does not measure NAS deduplication throughput or memory savings. The local modular model is not the built-in transformer_tiny, though it is a matched sequence-level modular architecture and both systems use identical training conditions. The mechanism signature evaluates one fixed proposed trace and one noncommuting alternative per trained seed.", "bench_report": { "bench_version": 1, "track": "sequence", "model": "modular_sequence_local", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003, "epochs": 12, "batch": 128 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12, "batch": 128 }, "mean": 0.15198808163404465 }, { "cfg": { "lr": 0.003, "epochs": 12, "batch": 128 }, "mean": 0.1417534351348877 }, { "cfg": { "lr": 0.01, "epochs": 12, "batch": 128 }, "mean": 0.15112163126468658 } ], "full": { "mean": 0.13169990107417107, "std": 0.017087285342305542, "per_seed": [ 0.1741165667772293, 0.12635251879692078, 0.13050207495689392, 0.13604258000850677, 0.11734391003847122, 0.11690718680620193, 0.1270616501569748, 0.1252727210521698 ], "n": 8 } }, "idea": { "per_seed": [ 0.1741165667772293, 0.12635251879692078, 0.13050207495689392, 0.13604258000850677, 0.11734391003847122, 0.11690718680620193, 0.1270616501569748, 0.1252727210521698 ], "mean": 0.13169990107417107, "std": 0.017087285342305542, "n": 8 }, "comparison": { "delta_mean": 0.0, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], "p_value": 1.0, "mde": 0.0, "mde_rel_pct": 0.0, "verdict": "no measurable effect", "system_worked": false }, "track_justification": "sequence is structurally matched: multi-token window correlations and a sequence-level model", "idea_sweep": [ { "cfg": { "lr": 0.001, "epochs": 12, "batch": 128 }, "mean": 0.14950868487358093 }, { "cfg": { "lr": 0.003, "epochs": 12, "batch": 128 }, "mean": 0.13169990107417107 }, { "cfg": { "lr": 0.01, "epochs": 12, "batch": 128 }, "mean": 0.14627121202647686 } ], "canonical_proposal": [ "g3", "g1", "global", "g0", "g2" ], "canonical_word": [ "g1", "g3", "global", "g0", "g2" ], "mechanism_signature": { "prediction": "disjoint-support swaps preserve the trained network output; group/global swaps do not", "predicted_commuting_max": 1e-05, "observed_commuting_max": 0.0, "predicted_noncommuting_positive": true, "observed_noncommuting_median": 1.8506829738616943, "confirmed": true, "n_trained_models": 8 } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }