EvoWorld: A World-Model-Centric Framework for Continuous Self-Evolution of Modular Embodied Skills

Current progress in physical intelligence largely relies onscaling monolithic Vision-Language-Action (VLA) models, yet real-worldpolicy data remain fragmented across scenes and tasks. This mismatchlimits transfer, exacerbates catastrophic forgetting, and impedes contin-ual improvement. A modular design that shares dynamics while special-izing skills is therefore a promising paradigm.We introduce EvoWorld (EvoW), a world-model-centric frameworkfor skill orchestration and iterative self-evolution. In EvoW, VLAs forman expandable library of pluggable experts. A high-level router selectsexperts conditioned on scene and task, while an action-conditioned videoworld model provides a shared dynamics prior for rollout-based planning.The world model provides counterfactual rollouts to score candidate ex-perts, while selected experts execute in the grounded scene to generatetrajectories for verification. A vision-language evaluator delivers seman-tic scoring and diagnostic tags, enabling targeted updates to the worldmodel, router memory, or specific experts rather than global retraining.This closes an automated loop that jointly improves grounding, routing,and skill refinement without manual task engineering. Experiments showthat EvoW enables automated task-to-policy synthesis with competi-tive success rates and consistent iterative gains in the evaluated settings,while producing valid and diverse trajectories that support evaluationand skill refinement.