Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework

Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.

Planning, a core component of intelligent behavior, remains challenging for LLMs, which often produce infeasible or incorrect solutions in long-horizon decision-making tasks due to inherent complexity.

In this paper, we propose a symbolic feedback-driven iterative self-refinement framework to enhance the robustness and reliability of LLMs in long-horizon planning.

Specifically, a natural language prompting mechanism is introduced to map logical symbols into natural language descriptions, enabling LLMs to better capture task constraints and semantics.

We further design a symbolic verifier that identifies errors and converts them into corrective instructions interpretable by the LLM, thereby guiding self-refinement.

In addition, we leverage a plan recognizer to infer goal reachability, facilitating more effective guidance toward desired goals.

Empirical results demonstrate that the proposed framework consistently improves both feasibility and correctness in long-horizon planning tasks.

This highlights its effectiveness in enhancing the reliability of LLM-based planning and potential to enable more trustworthy AI systems.