Tag
This paper proposes a symbolic feedback-driven iterative self-refinement framework to improve the robustness and reliability of large language models in long-horizon planning tasks. The method uses natural language prompting, a symbolic verifier, and a plan recognizer to enhance feasibility and correctness.