AILab:AFalsifiable,Anti-GoodhartProtocol forTestingFunctional/AutonomousIntentionalityin RLAgents

AILab – Functional/Autonomous Intentionality Lab (v0.9.1) is a reproducible, one-command evaluation pipeline for testing functional intentionality in reinforcement-learning agents under explicit anti-Goodhart controls. The project is zero-narrative: the primary output is a machine-readable decision artifact (runs*/decision.json) that deterministically summarizes thresholds, pass/fail counts across mandatory seeds, post-tests, and an overall verdict (GREEN / YELLOW / RED). No human interpretation is required for the verdict. What this release provides A runnable pipeline (batch over seeds) with a fixed decision procedure producing decision.json. Built-in Goodhart gates: T2 sham separation (true intrinsic signal must exceed sham controls), T3 sign-flip directionality (α>0 must outperform α