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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented an end-to-end tangent/projection control layer with an identical rnn_small architecture on a structurally matched custom evasion-dynamics track. The safety mechanism was confirmed on trained models: maximum one-step disk penetration fell from 0.05145 for baseline to 0.0 for the idea. However, test MSE was slightly worse (0.14498 vs 0.14170, paired delta +0.00328, permutation p=0.74865), so there is no significant benchmark win.", "metrics": { "baseline": "Best lr=0.01; test MSE mean 0.1416995758, std 0.0330424149.", "idea": "Best tested lr=0.01; test MSE mean 0.1449784860, std 0.0256713860; paired delta +0.0032789102, 4/8 wins, p=0.74865. Mechanism signature: baseline maximum one-step penetration 0.0514523461, idea 0.0, confirmed=true." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_tangent_stage2.py", "files": [ "bench_tangent_stage2.py", "bench_report.json" ], "limitations": "The custom track uses stationary pursuer disks and synthetic demonstrations rather than moving multi-agent pursuers, PPO, or a full rollout completion-time objective. The benchmark used 400 training and 200 test examples, 15 epochs, one shared rnn_small architecture, and three learning rates; path length, escape rate, samples-to-success, soft branch training, and the claimed speed-ratio transition were not evaluated at neural-network scale.", "bench_report": { "bench_version": 1, "track": "tangent_evasion_dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.2184290774 }, { "cfg": { "lr": 0.003 }, "mean": 0.2024546862 }, { "cfg": { "lr": 0.01 }, "mean": 0.142414717 } ], "full": { "mean": 0.1416995758, "std": 0.0330424149, "per_seed": [ 0.1762400717, 0.1883647144, 0.0758553222, 0.1291987598, 0.1371793747, 0.1444202363, 0.1206313521, 0.1617067754 ], "n": 8 } }, "idea": { "mean": 0.144978486, "std": 0.025671386, "per_seed": [ 0.1702807397, 0.1558924466, 0.0986566544, 0.1305036098, 0.1295443177, 0.1873809248, 0.1514844894, 0.1360847056 ], "n": 8 }, "comparison": { "delta_mean": 0.0032789102, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ -0.005959332, -0.0324722677, 0.0228013322, 0.00130485, -0.007635057, 0.0429606885, 0.0308531374, -0.0256220698 ], "p_value": 0.74865, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "predicted": "projection keeps one-step disk penetration at numerical zero", "observed_baseline_max_penetration": 0.0514523461, "observed_idea_max_penetration": 0.0, "confirmed": true }, "custom_track": { "name": "tangent_evasion_dynamics", "file": "bench_tangent_stage2.py", "domain": "dynamics" } }, "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }