Эксперимент: Gauge-Free Inverse OT Attention (#1163)

{ "worked": true, "confidence": 8, "verdict": "Built a reproducible NumPy MVP for log-domain Sinkhorn attention and gauge-free inverse-cost recovery. On a 9x7 synthetic OT plan, centered reconstruction error was 7.0e-16, gauge perturbation changed the plan by 3.6e-16 relative and the recovered cost by 7.6e-16; flooring increased error from 4.3e-10 at delta=1e-12 to 0.227 at delta=1e-3. The attention toy benchmark did not show a task win: row-softmax retrieval accuracy was 1.000 versus 0.648 for Sinkhorn, and the inverse penalty was evaluated rather than optimized, so the claimed training benefit remains unverified.", "metrics": { "baseline": "Row-softmax retrieval accuracy 1.000; mean row entropy 2.105", "idea": "Sinkhorn retrieval accuracy 0.6475; mean entropy 0.348; centered inverse recovery 7.02e-16 after 300 iterations; gauge-plan relative change 3.63e-16; gauge-inverse relative change 7.60e-16" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only synthetic NumPy experiments were run. No learned Transformer, backpropagated inverse-cost regularizer, FLOP/speed measurement, noisy-plan study, CUDA implementation, or realistic copy/patch-classification task was tested; the penalty branch is currently diagnostic rather than trained." }