Governing Simulator Discrepancy via Deterministic Pairing and Horizon-Validated Residual Dynamics

This work presents a governance-driven approach to sim-to-sim residual learning in robotics, addressing the failure modes caused by misaligned paired transitions across simulators. We introduce a deterministic pairing protocol that enforces strict episode and timestep correspondence between simulators, combined with a hard alignment gate that blocks training when pairing validity fails. A projection-consistent residual correction model is then applied to correct next-state discrepancies while preserving valid state geometry. Evaluation is performed using a horizon-validated protocol, including one-step accuracy, teacher-forced rollouts at multiple horizons (50/200/500), contact-regime slices, and free-running stability checks. The results demonstrate that residual correction remains stable and accurate under contact-rich conditions when governed by explicit pairing and validation constraints. This repository contains the reference implementation, datasets, and validation artifacts for reproducing the reported results.