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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built an end-to-end intrinsic S1 retraction intervention with the shared rnn_small architecture, shared learning-rate grid, baseline sweep, eight paired seeds, and permutation testing. The trained-model mechanism signature was confirmed: intrinsic maximum S1 norm violation was 1.19e-7 versus 0.1689 for baseline. However, test MSE was significantly worse for the idea: 0.0177347 versus 0.0062498, paired delta +0.0114849, p=0.0081, with 0/8 paired wins.", "metrics": { "baseline": "Best lr=0.01; full 8-seed test MSE mean 0.0062498191, std 0.0014967029.", "idea": "Best lr=0.01; full 8-seed test MSE mean 0.0177347455, std 0.0075728509; paired delta +0.0114849264, p=0.0081, idea wins 0/8. Signature confirmed: baseline max S1 violation 0.1689367294, idea max violation 1.1920928955e-7." }, "bench_report": { "bench_version": 1, "track": "manifold_pendulum", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.30809812992811203 }, { "cfg": { "lr": 0.003 }, "mean": 0.04099804675206542 }, { "cfg": { "lr": 0.01 }, "mean": 0.007313748239539564 } ], "full": { "mean": 0.0062498191255144775, "std": 0.0014967029373309296, "per_seed": [ 0.00792852696031332, 0.00787876546382904, 0.0054243444465100765, 0.008023356087505817, 0.00436387537047267, 0.006577811203896999, 0.004088760819286108, 0.005713112652301788 ], "n": 8 } }, "idea": { "mean": 0.017734745517373085, "std": 0.0075728509012216545, "per_seed": [ 0.024637481197714806, 0.015422593802213669, 0.03352177515625954, 0.011271449737250805, 0.01013706810772419, 0.020766805857419968, 0.011616603471338749, 0.014504186809062958 ], "n": 8 }, "comparison": { "delta_mean": 0.011484926391858608, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.016708954237401485, 0.007543828338384628, 0.02809743070974946, 0.0032480936497449875, 0.00577319273725152, 0.014188994653522968, 0.007527842652052641, 0.00879107415676117 ], "p_value": 0.0081, "mde": 0.006700379570878075, "mde_rel_pct": 107.20917575877058, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "custom_track": { "name": "manifold_pendulum", "file": "manifold_dynamics_track.py", "domain": "dynamics_and_embedded_manifolds" }, "idea_config": { "lr": 0.01 }, "prediction": "Intrinsic retraction keeps every predicted embedded angular state on S1; Euclidean output has nonzero norm error.", "predicted_baseline_violation_order": "nonzero", "predicted_idea_violation": 0.0, "observed_baseline_max_violation": 0.16893672943115234, "observed_idea_max_violation": 1.1920928955078125e-07, "observed_idea_metric_seed0": 0.024637481197714806, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "manifold_dynamics_track.py", "bench_report.json" ], "limitations": "Tested one custom S1-embedded pendulum prediction task, one rnn_small architecture, 18 epochs, and a three-value learning-rate grid. This benchmark did not train a neural Schrödinger-bridge controller, estimate endpoint KL, test Stiefel manifolds, compare sampling speed, or evaluate multimodal generative targets.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }