SCOPE: Auditable Score-Isolated Agentic Adaptation for Video World Models

Video world models are increasingly used as simulators for planning and em- bodied reasoning, but inference-time adaptation creates an evaluation challenge: jointly evolving controls obscure causal attribution and can entangle adaptation with held-out feedback. We introduce SCOPE (Score-Isolated Control Orchestra- tion with Provenance-Bound Evidence), which makes the inference harness an explicit typed state over text, sampling, verification, and selection. Development- supported edits update one axis at a time; the final policy and exact Base fallback are frozen before held-out scoring, with every registered transition bound to its evi- dence. On Physics-IQ, SCOPE achieves 34.94, improving on single-sample Base by +14.24 (95% CI [+8.10,+21.23]), including candidate expansion. A secondary comparison over the same candidate pool yields +7.53 over uniform selection (95% CI [+4.04,+11.52]). Matched ablations identify useful text, sampling, and learned-selection controls. Comparisons with the strongest agentic baseline and equal-budget random updates remain statistically unresolved, while prospective tests identify calibration under task and backbone shift as the next deployment chal- lenge. Together, these results establish an auditable basis for developing inference controls and separating candidate quality from deployment choice.