Safe Dynamic Decision-Region Resolution for Data-Efficient World Models

An agent can become better informed and still lose its last opportunity to make a justified decision. We ask what a world model must learn offline when some uncertainty can still be resolved safely at deployment, and formalize this as Safe Dynamic Decision-Region Resolution (SD-DRR): offline evidence needs to remove only the decision conflicts that safe online interaction cannot resolve within budget. A decision-nuisance factorization preserves the optimal resolution cost. In finite realizable games with deterministic passive observations, minimal budget obstructions characterize the required evidence, and their covering geometry yields optimal query allocations with matching exponential failure rates. Continuation responses specify the required prediction accuracy, and calibrated cost-risk frontiers extend the analysis to continuous stochastic histories and misspecified neural predictors. In finite games, SD-DRR needs $10.3\times$ fewer passive observations under a 90% retention criterion, and convex query allocation attains the predicted exponent. Response supervision raises TwoRoom success by 3.50 to 5.50 percentage points at three source budgets. On 400 fresh PushT windows, the role interface adds 15.75 percentage points at a fixed source budget, and convex inference raises the return of a frozen Reacher agent by 54.48 points over 400 episodes.