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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built the Normal-Space Quotient Encoder as a local SO(2) finite-difference orbit regularizer for the bench vision CNN. It reduced test error slightly, from 0.788125 to 0.780000, and reduced trained-model representation drift by 37.6%, but the paired permutation test was not significant (delta=-0.008125, p=0.42255), so this is not a benchmark win.", "metrics": { "baseline": "Test error mean 0.788125, std 0.047033; best lr 0.003.", "idea": "Test error mean 0.780000, std 0.047893; best lr 0.003, lambda 0.1.", "paired_delta": "-0.008125; 5/8 idea wins; permutation p=0.42255; verdict no significant win.", "mechanism_signature": "Baseline drift 1.663749; idea drift 1.037937; ratio 0.623854; confirmed=true.", "math": "Projector idempotence error 1.41e-12; orbit preservation error 1.00e-12; singular projector norm 0." }, "bench_report": { "bench_version": 1, "track": "vision", "model": "cnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.7749999911 }, { "cfg": { "lr": 0.003 }, "mean": 0.7600000054 }, { "cfg": { "lr": 0.006 }, "mean": 0.868750006 } ], "full": { "mean": 0.7881250083, "std": 0.0470330594, "per_seed": [ 0.7200000286, 0.7599999905, 0.8299999833, 0.7300000191, 0.7900000215, 0.8050000072, 0.8700000048, 0.8000000119 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.003, "lambda": 0.1 }, "sweep": [ { "cfg": { "lr": 0.003, "lambda": 0.03 }, "mean": 0.7537500113 }, { "cfg": { "lr": 0.003, "lambda": 0.1 }, "mean": 0.7537499964 }, { "cfg": { "lr": 0.003, "lambda": 0.3 }, "mean": 0.7887500077 } ], "full": { "mean": 0.7800000012, "std": 0.0478931041, "per_seed": [ 0.7300000191, 0.7549999952, 0.8399999738, 0.6899999976, 0.8000000119, 0.8000000119, 0.8299999833, 0.7950000167 ], "n": 8 } }, "comparison": { "delta_mean": -0.0081250072, "idea_wins": 5, "n_pairs": 8, "per_seed_diffs": [ 0.0099999905, -0.0049999952, 0.0099999905, -0.0400000215, 0.0099999905, -0.0049999952, -0.0400000215, -0.0049999952 ], "p_value": 0.42255, "mde": 0.0174447431, "mde_rel_pct": 2.21344874, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "predicted_effect": "orbit tangent penalty reduces local representation drift", "predicted_ratio": "< 1", "observed_baseline_drift": 1.6637493, "observed_idea_drift": 1.0379373, "observed_ratio": 0.6238544, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "The shared GPU convolution engine failed, so the protocol ran on CPU. The run used 400 training examples, 200 test examples, and 8 epochs; only the vision track was tested. The implementation used finite-difference spatial rotations, not learned transformation groups, adaptive rank selection, or non-identity metrics.", "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }