Эксперимент: Nonreversible latent instanton sampler (#1380)

{ "worked": true, "confidence": 6, "verdict": "Built a 2D nonequilibrium double-well MVP with state-dependent diagonal mobility, mobility-weighted Freidlin–Wentzell action, direct endpoint-constrained path optimization, and a reverse-relaxation baseline. The stochastic moment check matched the prescribed SDE, and action optimization reduced action from 14.27 for the straight path to 1.01 while perturbations increased it to 25.89. In 1,200 stochastic trials, the optimized-path controller reached the target in 98.75% of trials versus 0.17% for reverse relaxation, but the baseline often diverged, so this is a promising toy signal rather than a fair neural-model benchmark.", "metrics": { "baseline": "reverse-relaxation success=0.0017; finite trajectories=173/1200; mean minimum target distance among finite trajectories=1.262", "idea": "action-optimized path success=0.9875; finite trajectories=1200/1200; mean final target distance=0.148; mean minimum target distance=0.102; optimized action=1.007 versus straight-path action=14.274" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "optimized_path.csv" ], "limitations": "No encoder-decoder, learned neural drift, or learned mobility network was trained. The baseline reverse dynamics became numerically unstable and receives no analogous stabilizing path controller, so the success-rate gap may overstate the practical advantage. Only one 2D toy system, one seed, one discretization, and one target pair were tested; no FLOP-matched neural experiment or trajectory-diversity analysis was performed." }