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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented gain-weighted co-design for the standard dynamics track using the shared rnn_small architecture, learned exponential timestep normalization, measured model gradients, and greedy gain-based clustering. The idea reduced mean test MSE from 0.0131815 to 0.0124626, but the paired permutation test gave delta=-0.0007189 and p=0.778, hence no significant win. The mechanism signature was confirmed: normalized observed mu=17.1707 versus unscaled mu=18.1659, a 5.48% reduction, but this did not establish improved neural task performance.", "metrics": { "baseline": "Best lr=0.003; full 8-seed test MSE mean=0.0131814581, std=0.0064111947; per-seed=[0.01743450, 0.00642413, 0.02289337, 0.00446576, 0.01008942, 0.00904593, 0.01367798, 0.02142058]. Baseline sweep means: lr 0.001=0.26463024, 0.003=0.01280444, 0.006=0.01357241.", "idea": "Best lr=0.003; full 8-seed test MSE mean=0.0124626068, std=0.0066104955; per-seed=[0.01393847, 0.00607969, 0.00940709, 0.00654941, 0.01659300, 0.00961433, 0.00987420, 0.02764466]. Idea sweep means: lr 0.001=0.26719950, 0.003=0.00899367, 0.006=0.01068951. Paired delta=-0.0007188513, 4/8 wins, permutation p=0.778." }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.264630239456892 }, { "cfg": { "lr": 0.003 }, "mean": 0.012804438127204776 }, { "cfg": { "lr": 0.006 }, "mean": 0.013572414871305227 } ], "full": { "mean": 0.013181458110921085, "std": 0.006411194717789758, "per_seed": [ 0.01743449829518795, 0.00642412668094039, 0.02289336547255516, 0.004465762060135603, 0.010089418850839138, 0.00904592964798212, 0.013677984476089478, 0.02142057940363884 ], "n": 8 } }, "idea": { "mean": 0.01246260676998645, "std": 0.006610495470253576, "per_seed": [ 0.013938470743596554, 0.006079694721847773, 0.00940709188580513, 0.00654940539970994, 0.016593003645539284, 0.009614327922463417, 0.009874203242361546, 0.027644656598567963 ], "n": 8, "best_cfg": { "lr": 0.003 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.2671995013952255 }, { "cfg": { "lr": 0.003 }, "mean": 0.008993665687739849 }, { "cfg": { "lr": 0.006 }, "mean": 0.010689508053474128 } ] }, "comparison": { "delta_mean": -0.0007188513409346342, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ -0.0034960275515913963, -0.00034443195909261703, -0.01348627358675003, 0.002083643339574337, 0.006503584794700146, 0.0005683982744812965, -0.003803781233727932, 0.006224077194929123 ], "p_value": 0.778, "mde": 0.005384277405441072, "mde_rel_pct": 40.847358161234816, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "trained-model gain weighting lowers normalized local certificate versus q=0", "observed_normalized_mu": 17.170748114585876, "observed_unscaled_mu": 18.16585087776184, "relative_reduction": 0.05477875877502347, "clusters_from_trained_models": [ [ 0, 1, 5 ], [ 2, 4 ], [ 3, 6, 7 ] ], "confirmed": true } }, "how_to_run": "cd /home/maxwelhelp/all/math2nn/experiments/exp1042_gain-weighted-cluster-co-design && /home/maxwelhelp/main/bin/python3 bench_experiment.py > bench_report.json", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "Only the matched standard dynamics track was tested; no tabular, vision, sequence, or custom track was run. The gain estimator uses input-gradient proxies rather than full block JVPs, clustering is used as a regularization signal rather than an actual sparse communication architecture, and the experiment used a small 400/200 dataset with 5 epochs. The idea and baseline were not forced onto identical execution devices because the canonical baseline train_model may select CUDA while the intervention used CPU for stability.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }