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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented a matched topological fluctuation recurrent system on the structurally appropriate dynamics track, using the canonical pendulum rollout benchmark and an equal-budget baseline learning-rate sweep. Across 8 paired seeds, the idea was significantly worse: test MSE 0.002484 versus baseline 0.001892, delta +0.000591, permutation p=0.0081. The trained-model perturbation signature was also not confirmed, with response ratio 1.0069 rather than the predicted <=1.0.", "metrics": { "baseline": "Test MSE mean 0.0018923835, std 0.0005824769; best config lr=0.006, epochs=18, weight_decay=0.", "idea": "Test MSE mean 0.0024836512, std 0.0007913987; best config lr=0.006, epochs=18, weight_decay=0., q=0.25, noise=0.02; paired delta +0.0005912678; permutation p=0.0081; 0/8 idea wins." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json", "topological_fluctuation.py" ], "limitations": "Only the built-in dynamics track was tested; no vision, sequence, tabular, boundary-strip, finite-periodogram, learned Chern-number objective, or robustness sweep was run. The intervention uses a compact recurrent approximation of stable chiral latent dynamics rather than a spatial 2D graph Fourier operator, because the standard dynamics bench has no explicit periodic spatial graph. The mechanism signature measures trained-model perturbation response but does not establish covariance-band Chern topology at neural-network scale.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 18, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.0015, "epochs": 18, "weight_decay": 0.0 }, "mean": 0.009193785954266787 }, { "cfg": { "lr": 0.003, "epochs": 18, "weight_decay": 0.0 }, "mean": 0.0050034308223985136 }, { "cfg": { "lr": 0.006, "epochs": 18, "weight_decay": 0.0 }, "mean": 0.002262153459014371 } ], "full": { "mean": 0.001892383472295478, "std": 0.0005824768770598943, "per_seed": [ 0.0016680180560797453, 0.0018032685620710254, 0.002757444279268384, 0.0028198829386383295, 0.0014833298046141863, 0.0013202878180891275, 0.0011803944362327456, 0.0021064418833702803 ], "n": 8 } }, "idea": { "mean": 0.0024836512457113713, "std": 0.0007913986756607849, "per_seed": [ 0.0018633758882060647, 0.002772978274151683, 0.003546620486304164, 0.003868584055453539, 0.00169182859826833, 0.001610392238944769, 0.002222771057859063, 0.002292659366503358 ], "n": 8, "best_cfg": { "lr": 0.006, "epochs": 18, "weight_decay": 0.0, "q": 0.25, "noise": 0.02 }, "sweep": [ { "cfg": { "lr": 0.006, "epochs": 18, "weight_decay": 0.0, "q": 0.25, "noise": 0.02 }, "mean": 0.0024836512457113713 }, { "cfg": { "lr": 0.006, "epochs": 18, "weight_decay": 0.0, "q": 0.5, "noise": 0.02 }, "mean": 0.0025042231136467308 }, { "cfg": { "lr": 0.006, "epochs": 18, "weight_decay": 0.0, "q": 0.75, "noise": 0.02 }, "mean": 0.0025412623363081366 } ] }, "comparison": { "delta_mean": 0.0005912677734158933, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0001953578321263194, 0.0009697097120806575, 0.00078917620703578, 0.0010487011168152094, 0.0002084987936541438, 0.00029010442085564137, 0.0010423766216263175, 0.00018621748313307762 ], "p_value": 0.0081, "mde": 0.000339375496055173, "mde_rel_pct": 17.933759252478964, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "prediction": "dissipative chiral latent dynamics should remain stable and reduce local perturbation response", "trained_model_observation": { "baseline": { "perturbation": 0.01, "response_rms": 8.569472265662625e-05, "output_rms": 0.1667313128709793 }, "idea": { "perturbation": 0.01, "response_rms": 8.628764771856368e-05, "output_rms": 0.16530634462833405 } }, "predicted_response_ratio": "<= 1.0", "observed_response_ratio": 1.0069190264772103, "confirmed": false }, "structural_match": "dynamics: stability/control and multi-step pendulum rollout" }, "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }