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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local custom braid-presentation classification track and matched raw-generator versus positive Garside-factor systems using the same MLP, training loop, epochs, batch size, and shared learning-rate sweep. The trained factor model showed exact Delta-prefix prediction invariance and a 3.49x generator-to-factor compression ratio, confirming the mechanism quantitatively, but its test error was slightly worse than baseline and not significant (paired delta +0.00375, permutation p=0.85155).", "metrics": { "baseline": "test err mean 0.50156, std 0.02225; best lr=0.001; sweep means lr=0.001: 0.50812, lr=0.003: 0.51937, lr=0.01: 0.51312", "idea": "test err mean 0.50531, std 0.04101; best lr=0.003; paired delta +0.00375; idea wins 2/8; permutation p=0.85155; trained prediction agreement under inserted Delta prefix=1.0; observed generator/factor ratio=3.4895" }, "bench_report": { "bench_version": 1, "track": "braid_garside_classification", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.508124977350235 }, { "cfg": { "lr": 0.003 }, "mean": 0.5193749815225601 }, { "cfg": { "lr": 0.01 }, "mean": 0.513124980032444 } ], "full": { "mean": 0.5015624798834324, "std": 0.0222534158322699, "per_seed": [ 0.48249998688697815, 0.48499998450279236, 0.5374999642372131, 0.5274999737739563, 0.5224999785423279, 0.4899999797344208, 0.4749999940395355, 0.492499977350235 ], "n": 8 } }, "idea": { "mean": 0.5053124837577343, "std": 0.041011381244236006, "per_seed": [ 0.512499988079071, 0.4899999797344208, 0.5324999690055847, 0.4099999964237213, 0.550000011920929, 0.492499977350235, 0.5374999642372131, 0.5174999833106995 ], "n": 8 }, "comparison": { "delta_mean": 0.0037500038743019104, "idea_wins": 2, "n_pairs": 8, "per_seed_diffs": [ 0.030000001192092896, 0.004999995231628418, -0.004999995231628418, -0.11749997735023499, 0.027500033378601074, 0.002499997615814209, 0.06249997019767761, 0.025000005960464478 ], "p_value": 0.85155, "mde": 0.044624401409542755, "mde_rel_pct": 8.89707727338653, "verdict": "no measurable effect", "system_worked": false }, "custom_track": { "name": "braid_garside_classification", "file": "garside_track.py", "domain": "algebraic braid presentations" }, "protocol_note": "Baseline and idea use the same MLP, seed data, epochs, batch size, and shared learning-rate grid; only representation differs.", "mechanism_signature": { "trained_factor_prediction_agreement": 1.0, "observed_generator_to_factor_ratio": 3.489510438346376, "expected_invariance": 1.0, "confirmed": true }, "idea_sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.5181249752640724 }, { "cfg": { "lr": 0.003 }, "mean": 0.48624998331069946 }, { "cfg": { "lr": 0.01 }, "mean": 0.5106249898672104 } ] }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py", "files": [ "garside_track.py", "run_bench.py", "bench_report.json" ], "limitations": "The custom benchmark uses a small B4 positive-word task and permutation-backed factorization rather than a production arbitrary signed-word Garside normal-form implementation. It evaluates histogram features with an MLP, not a Transformer or attention-FLOP/runtime tradeoff, and tests literal left Delta-prefix augmentation rather than arbitrary braid rewrites or right multiplication.", "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }