Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics

This paper explores methods for controlling the long-term behavior of language model agents by incorporating explicit state dynamics. It aims to improve the predictability and reliability of AI agents in sequential decision-making tasks, which is crucial for their deployment in real-world scenarios requiring sustained and coherent actions.