Proposes a world model that LLM agents use to predict action consequences before execution, and which self-evolves from interaction data rather than relying on a fixed predictor for long-horizon planning. Directly targets the foresight gap that causes agent planning failures, making it builder-relevant for anyone running multi-step agentic workflows.