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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Implemented Active-Set CG Router as an end-to-end optimizer intervention on the structurally matched tabular MLP track. Against the tuned Adam baseline, it reduced test MSE from 7.8484 to 7.0839, with paired delta -0.7645, 8/8 wins, and permutation p=0.0081. The router coefficients were feasible to numerical precision and sparse, but this is only a small tabular benchmark and does not establish MoE downstream gains or production routing speedups.", "metrics": { "baseline": "Adam, 8-seed full test MSE 7.8483812213; sweep over lr={0.001,0.003,0.01} and weight_decay={0,0.0001}.", "idea": "Active-set simplex router, 8-seed full test MSE 7.0839007497; paired delta -0.7644804716; 8/8 wins; permutation p=0.0081; mean simplex error 8.33e-17; mean support 2.875/16; mean pivots 13.625." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_active_set_router.py", "files": [ "bench_active_set_router.py", "bench_report.json", "active_set_router.py", "verify_mechanism.py", "mechanism_results.json", "results.json" ], "limitations": "Only the tabular Friedman#1 track and mlp_tiny were tested. No vision/sequence/dynamics track, real MoE experts, GPU-vs-CPU speed/FLOP accounting, matrix-free CG implementation in the benchmark loop, Adam/softmax router alternatives beyond the implemented Adam baseline, or downstream inference-cost measurement was tested.", "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0001, "epochs": 12, "batch": 64, "lam": 0.001 }, "full": { "mean": 7.848381221294403, "std": 0.797417166889868, "per_seed": [ 7.573554515838623, 8.29439926147461, 7.347502708435059, 7.371220588684082, 6.545068740844727, 7.915830612182617, 9.391494750976562, 8.347978591918945 ], "n": 8 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 18.613102436065674 }, { "cfg": { "lr": 0.001, "weight_decay": 0.0001, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 18.691447734832764 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 13.314521551132202 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0001, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 13.184970617294312 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 7.832738399505615 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 7.646669268608093 } ] }, "idea": { "cfg": { "lr": 0.01, "weight_decay": 0.0001, "epochs": 12, "batch": 64, "lam": 0.001 }, "mean": 7.08390074968338, "std": 0.49960844624863177, "per_seed": [ 6.786007404327393, 7.105201244354248, 7.026352882385254, 6.861461162567139, 6.146136283874512, 7.232509613037109, 7.602020263671875, 7.911517143249512 ], "n": 8 }, "comparison": { "delta_mean": -0.764480471611023, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.7875471115112305, -1.1891980171203613, -0.3211498260498047, -0.5097594261169434, -0.39893245697021484, -0.6833209991455078, -1.7894744873046875, -0.4364614486694336 ], "p_value": 0.0081, "mde": 0.416972102836185, "mde_rel_pct": 5.312842114560478, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "track_match": "tabular optimizer/training-procedure structure", "prediction": "simplex router remains feasible and active-set identifies sparse support", "observed": { "mean_simplex_error": 8.326672684688674e-17, "min_x": 0.0, "mean_support": 2.875, "mean_pivots": 13.625 }, "confirmed": true } }, "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }