CWME: Continual World Model Evolution via Interaction Experience Learning

World models provide interactive agents with environmental knowledge to support reasoning and decision making. However, continual interaction introduces new states and outcomes that may reveal limitations in previously acquired knowledge. Two key challenges arise in continual world modeling: (1) Interaction feedback is insufficiently integrated to validate and refine existing world knowledge, limiting its continual evolution. (2) Task-relevant historical experience fails to adapt to evolving environmental states, making direct reuse unreliable and repeated reasoning costly. To address these challenges, we propose CWME, a framework for Continual World Model Evolution via Interaction Experience Learning. CWME maintains Situational Knowledge, Action Trajectory Experience, and Failure-Aware Knowledge. These forms of knowledge provide environmental constraints, behavioral priors, and reuse boundaries for future decisions. Knowledge-Informed Trajectory Generation adapts historical behavioral structure to the current interaction instead of replaying past solutions. Experience-Guided Decision Routing distinguishes retrieval relevance from current-state applicability and selects direct reuse, lightweight verification, or fresh reasoning accordingly. Evolving Interaction Experience Updating incorporates interaction outcomes to refine accumulated knowledge and the conditions under which it can be reused. Extensive experiments on the public interactive benchmarks demonstrate that CWME outperforms all evaluated baselines in task performance while improving reasoning efficiency, further demonstrating the benefits of continual knowledge refinement and state-aware experience reuse.