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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented and ran the Bellman stopping controller on the registered dynamics track using paired rnn_small systems and 8 seeds. The Bellman recursion had zero residual, and trained-model continuation frequency decreased monotonically with cost, but the tuned adaptive system significantly worsened test MSE versus the tuned fixed-stop baseline; therefore the idea does not win this benchmark.", "metrics": { "baseline": "best_cfg={lr:0.006, steps:1}; full mean test MSE=0.0020863086683675647, std=0.00045500750701757853", "idea": "best_cfg={lr:0.006, cost:0.003}; full mean test MSE=0.0035335189022589475, std=0.0006978225321427611", "comparison": "paired delta_mean=0.0014472102338913828, p_value=0.0081, idea_wins=0/8, verdict=idea worse (significant)" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json", "bench_run.log" ], "limitations": "The controller uses a lightweight self-consistency verifier proxy rather than an LLM, external verifier, or learned continuation-value MLP. Refinement costs are normalized proxy costs; wall-clock and token costs were not measured.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "steps": 1 }, "sweep": [ { "cfg": { "lr": 0.001, "steps": 1 }, "mean": 0.004103701037820429 }, { "cfg": { "lr": 0.001, "steps": 3 }, "mean": 0.005604122125077993 }, { "cfg": { "lr": 0.001, "steps": 5 }, "mean": 0.00564115314045921 }, { "cfg": { "lr": 0.003, "steps": 1 }, "mean": 0.0033861721749417484 }, { "cfg": { "lr": 0.003, "steps": 3 }, "mean": 0.005019772244850174 }, { "cfg": { "lr": 0.003, "steps": 5 }, "mean": 0.005090346734505147 }, { "cfg": { "lr": 0.006, "steps": 1 }, "mean": 0.0016983873210847378 }, { "cfg": { "lr": 0.006, "steps": 3 }, "mean": 0.00288440816802904 }, { "cfg": { "lr": 0.006, "steps": 5 }, "mean": 0.0029781226767227054 } ], "full": { "mean": 0.0020863086683675647, "std": 0.00045500750701757853, "per_seed": [ 0.0018287990242242813, 0.0014274335699155927, 0.0014271392719820142, 0.002110177418217063, 0.002619170816615224, 0.0024625908117741346, 0.002196874236688018, 0.00261828419752419 ], "n": 8 } }, "idea": { "mean": 0.0035335189022589475, "std": 0.0006978225321427611, "per_seed": [ 0.0029865081887692213, 0.002204712713137269, 0.003266959683969617, 0.003441720036789775, 0.0042303199879825115, 0.004240280482918024, 0.0034899020101875067, 0.0044077481143176556 ], "n": 8 }, "comparison": { "delta_mean": 0.0014472102338913828, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.00115770916454494, 0.0007772791432216763, 0.0018398204119876027, 0.001331542618572712, 0.0016111491713672876, 0.0017776896711438894, 0.0012930277734994888, 0.0017894639167934656 ], "p_value": 0.0081, "mde": 0.00031240367548733783, "mde_rel_pct": 14.973991155957691, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "math_check": { "max_bellman_residual": 0.0, "value_nonincreasing_with_cost": true, "stop_boundary_nonincreasing_with_cost": true, "costs": [ 0, 0.02, 0.035, 0.04, 0.05 ], "initial_values": [ 0.19714903424072264, 0.09714903424072266, 0.02214903424072264, 0.0037074088745117084, 0.0 ], "threshold_indices": [ 20, 12, 4, 2, 0 ] }, "state": "self-consistency verifier score from trained RNN successive refinement candidates", "predicted_vs_observed": { "predicted_continuation_decreases_with_cost": true, "costs": [ 0.0005, 0.0015, 0.003 ], "observed_continue_fraction_by_cost": [ 0.9968749955296516, 0.9940624907612801, 0.9871874675154686 ], "observed_predicted_gain_by_cost": [ 0.00294073588502215, 0.0030416790865274382, 0.003552038834823179 ], "observed_task_mse_gain_by_cost": [ -9.425566531717778e-08, -7.286260140696823e-07, -1.4213641406968235e-06 ] }, "confirmed": true }, "idea_sweep": { "best_cfg": { "lr": 0.006, "cost": 0.003 }, "sweep": [ { "cfg": { "lr": 0.006, "cost": 0.0005 }, "mean": 0.0035419926862232387 }, { "cfg": { "lr": 0.006, "cost": 0.0015 }, "mean": 0.003538758319336921 }, { "cfg": { "lr": 0.006, "cost": 0.003 }, "mean": 0.0035335189022589475 } ], "full": { "mean": 0.0035335189022589475, "std": 0.0006978225321427611, "per_seed": [ 0.0029865081887692213, 0.002204712713137269, 0.003266959683969617, 0.003441720036789775, 0.0042303199879825115, 0.004240280482918024, 0.0034899020101875067, 0.0044077481143176556 ], "n": 8 } }, "protocol_notes": { "structural_match": "dynamics: actuated pendulum rollout and sequential refinement", "epochs": 12, "n_train": 400, "n_test": 400, "paired_seeds": 8, "baseline_knobs_swept": [ "lr", "fixed refinement steps" ], "idea_knobs_swept": [ "lr", "Bellman cost" ] } }, "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }