Эксперимент: Particular-Integral Latent Reduction (#1361)

{ "worked": true, "confidence": 8, "verdict": "Built a fixed-seed toy neural ODE MVP with an explicit closure constraint f(z)=z_g and vector-field relation v_g=a(z)f(z), plus a linear numerical sanity check. At nearly equal parameter counts, closure reduced maximum long-horizon constraint drift from 0.0487166 to exactly 0.0, confirming the claimed invariant-manifold behavior. However, it worsened oscillator retained-coordinate error, so no overall accuracy win or demonstrated latent-dimension/FLOP reduction was established.", "metrics": { "baseline": "2,487 parameters; max constraint drift 0.0487166; final XY error 0.0466631; mean XY error 0.0220389", "idea": "2,467 parameters; max constraint drift 0.0; final XY error 0.0691875; mean XY error 0.0355780" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "README.md" ], "limitations": "Only a 3D toy oscillator with one manually chosen gauge coordinate was tested. The constraint head was hard-coded as f=z_g rather than learned, no projection or quotient-coordinate integration was implemented, and no MuJoCo task, NFE comparison, multiple seeds, or statistical confidence interval was evaluated." }