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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "The idea was tested on the registered sparse_cycle_compatibility graph-context classification track with identical GRU systems, matched batch/epochs, and a shared learning-rate union. The best orthogonal-memory result was worse than the tuned baseline (delta_mean +0.0400, p=0.0169), so the required significant-win criterion was not met; the mechanism signature is also unconfirmed because the baseline trained-model edge violation was not captured.", "metrics": { "baseline": "mean err 0.55375 over 8 seeds; best lr=0.01; per-seed [0.6650, 0.5750, 0.6300, 0.3300, 0.5200, 0.5400, 0.6350, 0.5350]", "idea": "best mean err 0.59375 over 8 seeds at lr=0.01, lambda_orth=0.0; per-seed [0.7300, 0.6100, 0.6900, 0.3700, 0.5700, 0.5950, 0.6300, 0.5500]; delta +0.0400; idea wins 1/8" }, "bench_report": { "bench_version": 1, "track": "sparse_cycle_compatibility", "model": "custom_gru", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.7281250052154064 }, { "cfg": { "lr": 0.003 }, "mean": 0.6506249976158142 }, { "cfg": { "lr": 0.01 }, "mean": 0.55375000461936 } ], "full": { "mean": 0.55375000461936, "std": 0.09809911813863173, "per_seed": [ 0.6650000214576721, 0.574999988079071, 0.6299999952316284, 0.33000001311302185, 0.5199999809265137, 0.5400000214576721, 0.6349999904632568, 0.5350000262260437 ], "n": 8 } }, "idea": { "mean": 0.5937500262260437, "std": 0.1005457494676594, "per_seed": [ 0.7300000190734863, 0.6100000143051147, 0.6899999976158142, 0.3700000047683716, 0.5699999928474426, 0.5950000286102295, 0.6299999952316284, 0.550000011920929 ], "n": 8, "lr": 0.01, "lambda_orth": 0.0, "edge_violation_mean": 0.8190760239958763 }, "comparison": { "delta_mean": 0.04000002145767212, "idea_wins": 1, "n_pairs": 8, "per_seed_diffs": [ 0.06500005722045898, 0.0350000262260437, 0.06000000238418579, 0.04000002145767212, 0.04999998211860657, 0.05500003695487976, -0.004999995231628418, 0.020000040531158447 ], "p_value": 0.0169, "mde": 0.019481796815583544, "mde_rel_pct": 3.792077363329512, "verdict": "idea worse (significant)", "system_worked": false }, "custom_track": { "name": "sparse_cycle_compatibility", "file": "bench/custom_tracks/sparse_cycle_compatibility.py", "domain": "masked_categorical_compatibility" }, "mechanism_signature": { "predicted_effect": "orthogonality penalty lowers exclusivity-edge embedding inner products", "baseline_edge_violation": null, "idea_edge_violation": 0.8190760239958763, "observed_reduction": null, "confirmed": false }, "protocol_notes": "8 paired seeds; registered graph-compatible track; identical GRU hidden width, epochs, batch, and learning-rate union; baseline tuned by sweep_baseline; idea evaluated over lr={0.001,0.003,0.01} and lambda={0,1,3}." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_orthogonal_memory.py", "files": [ "bench_orthogonal_memory.py", "bench_report.json", "bench_stdout.txt" ], "limitations": "The registered track provides one-event categorical inputs, so the experiment used duplicated two-step sequences and did not test long-range contextual retention. The custom GRU was local rather than a built-in model, although both systems shared it exactly. The mechanism signature lacks a baseline trained-model embedding measurement, so it cannot claim quantitative confirmation; lambda=0 was selected as the best idea setting, meaning the orthogonality intervention itself did not win.", "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }