Task-Conditioned Decision Geometry for World-Model Planning

World-model planning turns predicted consequences into actions by comparing candidates for the current task. Yet standard world models learn to predict what each candidate will cause independently of the task, whereas planning depends on task-conditioned differences between candidates. Under a finite representation budget, predictive training can therefore discard a small but decision-critical difference while retaining low prediction error. We address this mismatch with Predictive Co-formation of Task-Induced Geometry (PC-TIG), which jointly shapes a shared representation through task-independent factual prediction and task-conditioned candidate comparison. PC-TIG combines a factual world model with a query-conditioned scorer and supports Joint and prediction-priority updates for both feedback actions and complete action segments. Across five-seed stochastic navigation and multi-objective HalfCheetah experiments, PC-TIG improves task-conditioned closed-loop control and segment selection over matched predictive, value-aware, and geometry-learning baselines while maintaining accurate dynamics. Our results show that effective world models must preserve not only predicted consequences, but also the task-dependent distinctions needed to choose among them.