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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local continuous-action control track and an end-to-end benchmark runner using the canonical bench training path, with baseline and idea sharing the same MLP critic, learning-rate search space, seeds, and training budget. The baseline and scheduler had identical test MSE (0.0356233; paired delta 0, permutation p=1.0) because candidate-pool selection does not alter critic training, and the trained-network action-regret slope was -0.494 versus the predicted -1.0, so the mechanism signature was not confirmed. The idea therefore gives no observed benchmark win.", "metrics": { "baseline": "best_cfg lr=0.01, fixed candidate pool n=8; full 8-seed test MSE mean=0.035623283591121435, std=0.008640293958528833", "idea": "best_cfg lr=0.01, epsilon=0.03; full 8-seed test MSE mean=0.035623283591121435, std=0.008640293958528833; mean scheduled pool=60.0 (sizes 32-64)", "paired_comparison": "delta_mean=0.0, idea_wins=0/8, permutation p_value=1.0, verdict=no measurable effect", "mechanism_signature": "predicted trained-model candidate-regret slope=-1.0; observed mean=-0.4938379473634249; confirmed=false" }, "bench_report": { "bench_version": 1, "track": "poisson_action_value", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "n": 8 }, "sweep": [ { "cfg": { "lr": 0.001, "n": 8 }, "mean": 0.4739884063601494 }, { "cfg": { "lr": 0.001, "n": 16 }, "mean": 0.4739884063601494 }, { "cfg": { "lr": 0.001, "n": 32 }, "mean": 0.4739884063601494 }, { "cfg": { "lr": 0.001, "n": 64 }, "mean": 0.4739884063601494 }, { "cfg": { "lr": 0.001, "n": 128 }, "mean": 0.4739884063601494 }, { "cfg": { "lr": 0.003, "n": 8 }, "mean": 0.2955882400274277 }, { "cfg": { "lr": 0.003, "n": 16 }, "mean": 0.2955882400274277 }, { "cfg": { "lr": 0.003, "n": 32 }, "mean": 0.2955882400274277 }, { "cfg": { "lr": 0.003, "n": 64 }, "mean": 0.2955882400274277 }, { "cfg": { "lr": 0.003, "n": 128 }, "mean": 0.2955882400274277 }, { "cfg": { "lr": 0.01, "n": 8 }, "mean": 0.038012176752090454 }, { "cfg": { "lr": 0.01, "n": 16 }, "mean": 0.038012176752090454 }, { "cfg": { "lr": 0.01, "n": 32 }, "mean": 0.038012176752090454 }, { "cfg": { "lr": 0.01, "n": 64 }, "mean": 0.038012176752090454 }, { "cfg": { "lr": 0.01, "n": 128 }, "mean": 0.038012176752090454 } ], "full": { "mean": 0.035623283591121435, "std": 0.008640293958528833, "per_seed": [ 0.03652806580066681, 0.03965979069471359, 0.04228668659925461, 0.03357416391372681, 0.022456083446741104, 0.05217588320374489, 0.02869047038257122, 0.029615124687552452 ], "n": 8 } }, "idea": { "mean": 0.035623283591121435, "std": 0.008640293958528833, "per_seed": [ 0.03652806580066681, 0.03965979069471359, 0.04228668659925461, 0.03357416391372681, 0.022456083446741104, 0.05217588320374489, 0.02869047038257122, 0.029615124687552452 ], "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 }, "mechanism_signature": { "prediction": { "quantity": "trained-model candidate action regret", "kappa": 2.0, "d": 2.0, "slope": -1.0 }, "observed_slopes_per_seed": [ -0.7058185791140722, -0.5139916507443746, -0.48684283734935213, -0.5550921083271281, -0.3907645786398352, -0.27029026113176285, -0.4199711072919162, -0.6079324563089579 ], "observed_slope_mean": -0.4938379473634249, "confirmed": false, "scheduler_pool_sizes": [ 64, 64, 64, 64, 32, 64, 64, 64 ], "scheduler_pool_mean": 60.0, "selected_idea_cfg": { "lr": 0.01, "epsilon": 0.03 }, "custom_track": { "name": "poisson_action_value", "file": "poisson_action_track.py", "domain": "dynamics/control" }, "structural_match": "continuous-action control with a learned Q critic and isolated local action optimum" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 poisson_stage2.py", "files": [ "poisson_stage2.py", "poisson_action_track.py", "bench_report.json", "stage2_output.txt" ], "limitations": "This is a compact supervised learned-critic control benchmark, not a multi-step actor-critic or environment rollout. Candidate selection was evaluated for signature and pool behavior but was not coupled to a downstream rollout reward; consequently the primary bench metric is critic MSE and is invariant to the inference-only pool size. The custom track is local and was not promoted into the shared read-only bench.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }