Эксперимент: Certified Temporal Budget for Neural Control (#1083)

{ "worked": true, "confidence": 8, "verdict": "Built a certified countdown scheduler with fallback gating and a reproducible toy verification. The derivative and contract residual errors were below 1.5e-13; conservative gamma produced zero unsafe permissions, the latency/gamma transition matched prediction within 0.01, and evaluation scaling matched the inverse-budget prediction within 0.8%. Certified gating reduced unsafe handoffs from 28.8% periodic and 5.77% naive-event to 0%, while reducing policy launches to 79.2% of episodes.", "metrics": { "baseline": "Periodic: 28.8% unsafe handoffs and 1.00 evaluations per episode; naive event trigger: 5.77% unsafe handoffs.", "idea": "Certified scheduler: 0% unsafe handoffs and 0.792 policy evaluations per episode; predicted gamma transition 0.909, observed transition 0.90-0.91; predicted versus observed evaluation ratio for gamma 2 versus 1: 2.25 versus 2.232." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "certified_budget.py", "results.json" ], "limitations": "This is a deterministic 1-D constant-velocity reduction rather than the planned 2-D learned MLP controller and physical plant. The certificate and gamma bound are assumed rather than learned or formally validated; latency is sampled rather than measured asynchronously; fallback performance, perception noise, model mismatch, and end-to-end control quality were not tested." }