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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented and ran the full 8-seed custom simplicial cochain benchmark with a parity-matched baseline sweep. The mass-weighted projector satisfied the mathematical checks to machine precision and the trained-model boundary-residual signature was strongly confirmed, but test MSE improved only from 0.1284407 to 0.1276272 with paired permutation p=0.5406, so the effect was not statistically significant and does not count as a benchmark win.", "metrics": { "baseline": "Best full-grid baseline lr=0.003: MSE 0.1284407 ± 0.0103335 across 8 seeds.", "idea": "Best idea lr=0.003: MSE 0.1276272 ± 0.0117133 across 8 seeds; paired delta=-0.0008135, 4/8 wins, permutation p=0.5406.", "math_check": "d1*d0 max=0; compatible projector idempotence=4.05e-16; mass norm=1.0; commutation defects 8.40e-16 and 2.00e-16; independent-control defects 1.633.", "mechanism_signature": "Trained-model face-boundary RMS decreased from 0.6453003 baseline to 3.95e-08 idea, reduction 99.99999%, confirmed=true." }, "bench_report": { "bench_version": 1, "track": "simplicial_cochain_regression", "model": "cochain_mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.1318089496344328 }, { "cfg": { "lr": 0.003 }, "mean": 0.13333775848150253 }, { "cfg": { "lr": 0.01 }, "mean": 0.13918141461908817 } ], "full": { "mean": 0.1284407116472721, "std": 0.01033345480907241, "per_seed": [ 0.13917690515518188, 0.13619644939899445, 0.11503438651561737, 0.14294329285621643, 0.12111610919237137, 0.11670704931020737, 0.12120074033737183, 0.1351507604122162 ], "n": 8 } }, "idea": { "mean": 0.12762724328786135, "std": 0.011713296514767995, "per_seed": [ 0.13896797597408295, 0.13771608471870422, 0.10817594081163406, 0.14403699338436127, 0.12192557752132416, 0.11830909550189972, 0.11906668543815613, 0.13281959295272827 ], "n": 8 }, "comparison": { "delta_mean": -0.0008134683594107628, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ -0.000208929181098938, 0.0015196353197097778, -0.006858445703983307, 0.0010937005281448364, 0.0008094683289527893, 0.0016020461916923523, -0.0021340548992156982, -0.002331167459487915 ], "p_value": 0.5406, "mde": 0.002417293917190717, "mde_rel_pct": 1.8820309278799117, "verdict": "no measurable effect", "system_worked": false }, "math_check": { "incidence_d1_d0_max": 0.0, "compatible_idempotence": 4.0475970476012593e-16, "compatible_bound": 0.9999999999999999, "compatible_commutation_d0": 8.402395041642446e-16, "compatible_commutation_d1": 2.0014830212433607e-16, "independent_commutation_d0": 1.632993161855452, "independent_commutation_d1": 1.6329931618554514 }, "custom_track": { "name": "simplicial_cochain_regression", "file": "cochain_track.py", "domain": "simplicial_geometry_pde_like" }, "mechanism_signature": { "prediction": "trained projected edge representations have lower face-boundary residual", "predicted_direction": "idea < baseline", "baseline_boundary_rms": 0.6453002542257309, "idea_boundary_rms": 3.951021199100069e-08, "reduction_fraction": 0.9999999387723595, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "cochain_track.py", "stage2_bench.py", "bench_report.json" ], "limitations": "The custom task uses a filled triangle rather than larger refined meshes, PDE fields, or molecular graphs. The experiment used a small MLP and 24 epochs; FLOPs, gradient variance, learned subspaces, smoothing variants, and multi-layer GNN scaling were not tested.", "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }