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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the Representation-Invariant Authority Demand loss on the structurally matched dynamics track using the shared rnn_small architecture and completed the paired 8-seed protocol with a baseline sweep. The best baseline and idea achieved exactly the same test MSE, with paired delta 0 and permutation p=1.0, so there is no observed training win. Representation scaling was invariant on trained models, but the predicted threshold rho=1.0 did not match the observed demand maximum 0.375469; thus the mechanism signature is not quantitatively confirmed.", "metrics": { "baseline": "Best cfg lr=0.01, lam=0.0; full 8-seed test MSE 0.00045527595102612395 ± 0.0001763609215911445.", "idea": "Best 8-seed test MSE 0.00045527595102612395 ± 0.0001763609215911445; paired delta 0.0, p=1.0, 0/8 wins.", "mechanism": "31 boundary-like trained-model samples: mean demand 0.0986458 for h versus 0.0987528 for 2h, relative spread 0.108%; representation invariance held, but predicted threshold 1.0 versus observed maximum demand 0.375469." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 iad_bench.py", "files": [ "iad_bench.py", "bench_report.json", "bench_stdout.txt", "iad_experiment.py", "results.json" ], "limitations": "Only the built-in dynamics track was tested; no cart-pole policy training, long-horizon closed-loop safety rollout, adaptive boundary sampling, learned f/g estimation, actuator-scale sweep, or alternative valid barrier representations was evaluated. The auxiliary loss used a fixed approximate pendulum model and an energy barrier evaluated from predicted theta plus observed omega, rather than a full differentiable rollout barrier constraint.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "lam": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "lam": 0.0 }, "mean": 0.0019458123424556106 }, { "cfg": { "lr": 0.001, "lam": 0.02 }, "mean": 0.0018735561170615256 }, { "cfg": { "lr": 0.003, "lam": 0.0 }, "mean": 0.000998065312160179 }, { "cfg": { "lr": 0.003, "lam": 0.02 }, "mean": 0.0010661959095159546 }, { "cfg": { "lr": 0.01, "lam": 0.0 }, "mean": 0.0005159456450201105 }, { "cfg": { "lr": 0.01, "lam": 0.02 }, "mean": 0.00056726903130766 } ], "full": { "mean": 0.00045527595102612395, "std": 0.0001763609215911445, "per_seed": [ 0.0006657303310930729, 0.0006478183786384761, 0.00023686116037424654, 0.0005133727099746466, 0.0002487104502506554, 0.0004278800042811781, 0.00025996746262535453, 0.0006418671109713614 ], "n": 8 } }, "idea": { "mean": 0.00045527595102612395, "std": 0.0001763609215911445, "per_seed": [ 0.0006657303310930729, 0.0006478183786384761, 0.00023686116037424654, 0.0005133727099746466, 0.0002487104502506554, 0.0004278800042811781, 0.00025996746262535453, 0.0006418671109713614 ], "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": { "n_boundary_like": 31, "predicted_representation_ratio": 1.0, "observed_mean_demand_base": 0.09864581376314163, "observed_mean_demand_2h": 0.09875281900167465, "observed_relative_spread": 0.0010847416706383228, "predicted_threshold_rho": 1.0, "observed_threshold_rho_from_demand": 0.3754686415195465, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }