OTIP: A Falsifiable Anti-Goodhart Protocol for Testing Ontological Commitments via Interventions in Reinforcement Learning Agents

OTIP (Ontology Through Intervention Protocol) is a falsifiable, Anti-Goodhart evaluation protocol for testing whether reinforcement-learning agents develop and maintain functional ontological commitments (entities, relations, and causal mechanisms) that remain stable under distribution shift, support interventional (“do-operator”) reasoning, and resist metric hacking. This release (v0.1) provides a paper-ready protocol specification with: an episode-level commitment mechanism (hashed, append-only logs) for ontologies, probabilistic predictions, and explicit falsifiers, a sealed holdout evaluation design to reduce leakage and Goodharting, an out-of-band auditor for tampering detection (hash-chain integrity, violation reports), two environment families (a ground-truth Causal Playground with switchable causal laws; and an Operational Sandbox scored via hidden holdouts and auditing), pre-registered hypotheses H1–H4, acceptance thresholds, baselines, ablations, negative controls, and reproducibility checklists. OTIP is intended as a methodological benchmark for “machine ontology” in the functional sense (testable commitments that guide action), not as a claim about consciousness.DOI: 10.5281/zenodo.18196498.