World models can be continuously improved during deployment by learning from real interactions and filtering unreliable predictions, making LLM agents better at long-horizon planning without modifying the agent itself.
This paper presents WorldEvolver, a framework that improves LLM agent planning by maintaining and updating a world model at test time. The system uses three components—episodic memory (storing real transitions), semantic memory (learning rules from errors), and selective foresight (filtering unreliable predictions)—to provide better action consequence predictions without retraining the agent.