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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "The registered custom benchmark track completed the full 8-seed paired protocol with a tuned baseline and shared learning-rate search space. The compatibility-regularized smooth MLP achieved lower test MSE than the baseline on all 8 seeds, with a significant paired improvement; the trained-network cross-partial residual also fell substantially, confirming the predicted mechanism. This remains a centralized-critic regression proxy rather than an end-to-end multi-agent RL return evaluation.", "metrics": { "baseline": "lr=0.006; test MSE mean 0.0180528023, std 0.0030102786", "idea": "lr=0.006, lambda=0.05; test MSE mean 0.0147398898, std 0.0021974917", "paired_delta": "-0.0033129125", "permutation_p_value": 0.0081, "mechanism_residual": "baseline 0.2295021936; idea 0.0556863118; confirmed=true" }, "bench_report": { "bench_version": 1, "track": "multi_agent_critic_compatibility", "model": "smooth_mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.06973899155855179 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.027223403099924326 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.017718153772875667 } ], "full": { "mean": 0.01805280230473727, "std": 0.003010278601654062, "per_seed": [ 0.013723039999604225, 0.01541468221694231, 0.020424267277121544, 0.021310625597834587, 0.02256813645362854, 0.015233941376209259, 0.01891121082007885, 0.016836514696478844 ], "n": 8 } }, "idea": { "mean": 0.014739889767952263, "std": 0.002197491715824413, "per_seed": [ 0.012573308311402798, 0.013758777640759945, 0.017280390486121178, 0.018282605335116386, 0.013562905602157116, 0.011569413356482983, 0.01632225140929222, 0.01456946600228548 ], "n": 8 }, "comparison": { "delta_mean": -0.0033129125367850065, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.0011497316882014275, -0.0016559045761823654, -0.003143876791000366, -0.0030280202627182007, -0.009005230851471424, -0.0036645280197262764, -0.0025889594107866287, -0.002267048694193363 ], "p_value": 0.0081, "mde": 0.0020403039006823757, "mde_rel_pct": 11.30186807699642, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "prediction": "compatibility penalty lowers trained-NN cross-partial residual; task MSE may trade off against fit", "baseline_mean_residual": 0.22950219362974167, "idea_mean_residual": 0.055686311796307564, "predicted_vs_observed": { "predicted_direction": "lower residual", "observed_direction": "lower residual" }, "confirmed": true }, "custom_track": { "name": "multi_agent_critic_compatibility", "file": "compat_track.py", "domain": "dynamics/control" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py", "files": [ "compat_track.py", "bench_run.py", "bench_report.json" ], "limitations": "The custom task is a fast centralized-critic regression proxy with two scalar interaction coordinates, not a full multi-agent RL simulator. No environment return, actor-gradient variance, replay sample efficiency, high-dimensional Hutchinson estimation, or wall-clock comparison against MADDPG/MASAC was tested. The smooth tanh MLP was necessary because ReLU second derivatives made the penalty identically inactive.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }