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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "The Uniform Stochastic Barrier Critic was implemented as an auxiliary stochastic barrier-loss intervention on the registered dynamics/rnn_small benchmark. The finite-chain sanity check passed, but the full paired benchmark showed no measurable task-metric effect: the best idea exactly matched the tuned baseline, with permutation p=1.0. The held-out barrier drift remained positive, so the proposed certificate mechanism was not confirmed.", "metrics": { "baseline": "best_cfg={lr:0.01, weight_decay:0.0}; full mean MSE=0.000540327702765353; per-seed=[0.0005864710547029972,0.0004609658499248326,0.000695098948199302,0.0006717051728628576,0.0005945063894614577,0.00035726596252061427,0.0004858067259192467,0.0004708015185315162]", "idea": "best_cfg={lr:0.01, lam:2.0}; full mean MSE=0.000540327702765353; per-seed=[0.0005864710547029972,0.0004609658499248326,0.000695098948199302,0.0006717051728628576,0.0005945063894614577,0.00035726596252061427,0.0004858067259192467,0.0004708015185315162]", "paired_delta": "0.0", "permutation_p_value": "1.0", "mechanism_signature": "predicted_mean_drift=0.00018591515208754572; heldout_mean_drift=0.00018507556751501397; margin=0.02; confirmed=false", "math_sanity": "failure_probability=0.6666666666666666, barrier=0.7, one_step_drift=-0.01, bound_holds=true" }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.00222656229743734 }, { "cfg": { "lr": 0.001, "weight_decay": 0.0001 }, "mean": 0.002201732073444873 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.0012092416291125119 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0001 }, "mean": 0.0011863615218317136 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 0.0006035602564224973 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001 }, "mean": 0.0006214260865817778 } ], "full": { "mean": 0.000540327702765353, "std": 0.00010833688702413184, "per_seed": [ 0.0005864710547029972, 0.0004609658499248326, 0.000695098948199302, 0.0006717051728628576, 0.0005945063894614577, 0.00035726596252061427, 0.0004858067259192467, 0.0004708015185315162 ], "n": 8 } }, "idea": { "mean": 0.000540327702765353, "std": 0.00010833688702413184, "per_seed": [ 0.0005864710547029972, 0.0004609658499248326, 0.000695098948199302, 0.0006717051728628576, 0.0005945063894614577, 0.00035726596252061427, 0.0004858067259192467, 0.0004708015185315162 ], "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": "training enforces nonpositive expected barrier drift on continuation states; held-out drift should be lower than zero", "predicted_mean_drift": 0.00018591515208754572, "heldout_mean_drift": 0.00018507556751501397, "margin": 0.02, "confirmed": false }, "idea_sweep": [ { "cfg": { "lr": 0.001, "lam": 0.5 }, "result": { "mean": 0.0021250569116091356, "std": 0.000452414474028617, "per_seed": [ 0.0021977173164486885, 0.0023012966848909855, 0.0026522083207964897, 0.0017550268676131964, 0.002219437388703227, 0.0011543183354660869, 0.002609655261039734, 0.0021107951179146767 ], "n": 8 } }, { "cfg": { "lr": 0.003, "lam": 1.0 }, "result": { "mean": 0.0011597898919717409, "std": 0.0003122565544309852, "per_seed": [ 0.0009158510947600007, 0.001064310665242374, 0.0017766037490218878, 0.001080201007425785, 0.0010183502454310656, 0.0006965000065974891, 0.0013033286668360233, 0.0014231737004593015 ], "n": 8 } }, { "cfg": { "lr": 0.01, "lam": 2.0 }, "result": { "mean": 0.000540327702765353, "std": 0.00010833688702413184, "per_seed": [ 0.0005864710547029972, 0.0004609658499248326, 0.000695098948199302, 0.0006717051728628576, 0.0005945063894614577, 0.00035726596252061427, 0.0004858067259192467, 0.0004708015185315162 ], "n": 8 } } ], "math_sanity": { "claim": "E[B_next]-B <= 0 implies failure probability <= B", "expected_B_next_state0": 0.69, "B_state0": 0.7, "drift_state0": -0.010000000000000009, "failure_probability": 0.6666666666666666, "bound_holds": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "The registered dynamics track is supervised pendulum rollout prediction rather than closed-loop policy training, so actual reach-avoid safety and distribution-shift performance were not tested. The barrier transition uses a hand-specified local pendulum proxy with finite disturbance sampling; adversarial state search, formal neural bounds, and large-scale training were not performed.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }