EP335: How AI agents learn from failure

Title: Self-Evolving World Models for LLM Agent PlanningSource: http://arxiv.org/abs/2606.30639v1Summary:This work presents WorldEvolver, a novel framework that equips long-horizon LLM agents with self-evolving world models to enhance predictive planning. By integrating episodic and semantic memories to revise test-time reasoning context dynamically without parameter updates, it establishes a robust new primitive for agent foresight.