Эксперимент: Bidirectional Conditional Cycle Loss (#1146)

{ "worked": true, "confidence": 8, "verdict": "Built a small PyTorch implementation of bidirectional conditional cycle loss using log-softmax probabilities and the paper’s eight-term compatibility residual. The exact-conditionals sanity check gave maximum absolute residual 6.7e-16, while random incompatible conditionals had mean absolute residual 4.27. On a fixed positive synthetic joint, lambda=0.5 reduced held-out 95th-percentile residual from 0.180 to 0.0724 (about 2.5x) and held-out mean residual from 0.0770 to 0.0285, with nearly unchanged task NLL and a tiny joint-L1 improvement from 0.1266 to 0.1257. This is a real compatibility signal, but it does not meet the proposed 10x threshold and is not evidence of a large accuracy gain.", "metrics": { "baseline": "heldout p95 |Delta|=0.1798533; heldout mean |Delta|=0.0769958; joint L1=0.1265701; task NLL=1.3049408", "idea": "lambda=0.5: heldout p95 |Delta|=0.0723756; heldout mean |Delta|=0.0284948; joint L1=0.1257352; task NLL=1.3050591; exact math check max |Delta|=6.66e-16" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 cycle_experiment.py", "files": [ "cycle_experiment.py", "results.json" ], "limitations": "Only a 4x4 synthetic categorical joint and one random seed/lambda were tested. The experiment did not test MNIST, larger neural architectures, calibration, sampling consistency, FLOPs/speed, shuffled-quadruple controls, or robustness across multiple datasets and seeds." }