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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the exact weighted linear-probe coreset on the structurally matched tabular MLP track, using bench.train_model and parity-matched three-point learning-rate sweeps. The trained-embedding mechanism was confirmed: observed mean support 69 matched the estimated (m+1)r bound of 69, with mean relative normal-equation residual 1.36e-11. The idea had worse mean test MSE, delta +0.088573, and permutation p=0.6962, so it did not achieve a significant win.", "metrics": { "baseline": "Best lr=0.003, epochs=10; 8-seed test MSE mean 7.136611352879902, std 0.942271126563791.", "idea": "Best lr=0.001, epochs=10; 8-seed test MSE mean 7.225184601729713, std 0.8393894535095389; paired delta +0.08857324884981232; 4/8 wins; permutation p=0.6962; verdict no significant win." }, "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003, "epochs": 10 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 10 }, "mean": 7.220232725842463 }, { "cfg": { "lr": 0.003, "epochs": 10 }, "mean": 7.203192111793281 }, { "cfg": { "lr": 0.01, "epochs": 10 }, "mean": 7.781077191271562 } ], "full": { "mean": 7.136611352879902, "std": 0.942271126563791, "per_seed": [ 6.400019744607703, 5.8703252148798235, 7.538179208923518, 9.004244278762076, 7.2321342357711815, 6.160751975873095, 7.162807354308972, 7.724428809912834 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.001, "epochs": 10 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 10 }, "mean": 7.222485799604187 }, { "cfg": { "lr": 0.003, "epochs": 10 }, "mean": 7.255961504565217 }, { "cfg": { "lr": 0.01, "epochs": 10 }, "mean": 7.751476359508167 } ], "full": { "mean": 7.225184601729713, "std": 0.8393894535095389, "per_seed": [ 7.0379122900681095, 5.62935485908627, 7.541481788855412, 8.681194260406958, 6.721253156819834, 7.15528821919516, 7.9675634823270185, 7.06742875707894 ], "n": 8 } }, "comparison": { "delta_mean": 0.08857324884981232, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 0.6378925454604065, -0.24097035579355364, 0.0033025799318942006, -0.3230500183551186, -0.5108810789513472, 0.9945362433220657, 0.8047561280180462, -0.6570000528338946 ], "p_value": 0.6962, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "predicted_support_bound": 69.0, "observed_mean_support": 69.0, "observed_mean_normal_residual_rel": 1.3569193552173906e-11, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the built-in tabular Friedman regression track and mlp_tiny were tested; no vision, sequence, dynamics, larger embeddings, repeated-refit timing, peak-memory measurement, noisy/ill-conditioned rank sweeps, or NNLS support-exchange fallback was tested.", "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }