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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built an end-to-end MI-gradient latent protection intervention for the structurally matched sequence track using the shared transformer_tiny architecture, fixed average latent noise variance, and an identical learning-rate union sweep. Across 8 paired seeds, MI allocation was effectively uniform (q approximately 1 for every latent coordinate) and did not improve test MSE: delta_mean was -0.0000061 with no significant paired win. The mechanism signature is not quantitatively confirmed because its predicted latent noise power and observed output perturbation MSE are different quantities, so the apparent ratio is not valid evidence.", "metrics": { "baseline": "sequence/transformer_tiny, best lr=0.001, test MSE 0.559855 +/- 0.025237; per-seed [0.572989, 0.592521, 0.550092, 0.567472, 0.569134, 0.516344, 0.584116, 0.526173]", "idea": "MI inverse-variance protection, best lr=0.001, test MSE 0.559849 +/- 0.025274; per-seed [0.573036, 0.592518, 0.550158, 0.567462, 0.569191, 0.516342, 0.584118, 0.525968]", "paired": "delta_mean=-0.0000061, idea wins=4/8; permutation p-value was non-significant; idea sweep means lr=0.001: 0.559849, lr=0.003: 0.671173, lr=0.006: 1.010021", "noise_budget": "Both systems used average latent variance 0.20; MI final variances were approximately 0.20 on all 64 coordinates." }, "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.5707684904336929 }, { "cfg": { "lr": 0.003 }, "mean": 0.6878322213888168 }, { "cfg": { "lr": 0.006 }, "mean": 1.0211272835731506 } ], "full": { "mean": 0.5598551481962204, "std": 0.02523705019158861, "per_seed": [ 0.5729890465736389, 0.5925205945968628, 0.5500922203063965, 0.5674721002578735, 0.5691339373588562, 0.5163435935974121, 0.5841162204742432, 0.52617347240448 ], "n": 8 } }, "idea": { "mean": 0.5598490461707115, "std": 0.025273703128400025, "per_seed": [ 0.5730356574058533, 0.5925182104110718, 0.5501575469970703, 0.5674620866775513, 0.5691909790039062, 0.516342043876648, 0.5841178894042969, 0.5259679555892944 ], "n": 8 }, "comparison": { "delta_mean": -6.102025508880615e-06, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 4.661083221435547e-05, -2.384185791015625e-06, 6.532669067382812e-05, -1.0013580322265625e-05, 5.704164505004883e-05, -1.5497207641601562e-06, 1.6689300537109375e-06, -0.00020551681518554688 ], "p_value": 1.0, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "predicted_latent_noise_power": 12.799999237060547, "observed_output_perturbation_mse": 0.06943081552162766, "predicted_vs_observed_ratio": 0.00542428278593961, "confirmed": false } }, "how_to_run": "cd /home/maxwelhelp/all/math2nn/experiments/exp2392_mi-guided-latent-protection && /home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json", "bench_stdout.txt" ], "limitations": "Only the sequence track was tested. The MI estimator was a task-loss gradient proxy rather than InfoNCE or a density-ratio estimator; quantization, bitrate allocation, random ranking, magnitude ranking, EMA ablations, and ranking stability were not tested. The mechanism signature's prediction and observation were not dimensionally matched, so it cannot establish the proposed quantitative mechanism.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }