Эксперимент: Resolution-Gated Dual Masking (#1333)

{ "worked": true, "confidence": 8, "verdict": "Built Resolution-Gated Dual Masking with plug-in conditional entropy, bootstrap gamma, validation-risk scoring, and the kappa/gamma selection rule. The binary-channel check matched the analytical information gain, and entropy-mask recovery degraded near p=0.4–0.49 while risk selection was generally more reliable; the gate consequently matched the risk baseline in the difficult regime. This validates the claimed crossover/safety behavior, but no prediction improvement over ordinary risk-based selection was observed in this toy experiment.", "metrics": { "baseline": "Risk-only selection: recovery at p=0.4 was 0.89, 0.1% MSE lower than entropy-only at n=256 averaged across distractor settings; at p=0.49 and n=256 recovery was 0.22 with MSE 0.247.", "idea": "Gated selection: recovery at p=0.4 was 0.89 and at p=0.49 was 0.22 for n=256; it matched risk selection because kappa was approximately 1 and/or gamma fell below threshold. Entropy recovery declined from 1.00 at p<=0.25 to 0.17 at p=0.49 for n=256, while the analytical entropy-gain check agreed within sampling error." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 resolution_gated_masking.py", "files": [ "resolution_gated_masking.py", "results.json" ], "limitations": "The predictor is an empirical conditional-mean lookup table rather than an MLP, and continuous quantile discretization, residual-resolution entropy, learned mixture-of-experts gating, FLOP/speed measurements, and threshold sweeps were not tested. The current toy setup uses an empty base mask, so kappa remains close to one; broader sequential mask-refresh experiments are needed to assess gains beyond safe fallback behavior." }