Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
Large language models (LLMs) often generate infeasible or incorrect solutions in long-horizon planning tasks. This paper proposes a symbolic feedback-driven iterative self-refinement framework that uses natural language prompting, a symbolic verifier, and a plan recognizer to significantly improve the feasibility and correctness of LLM planning, enhancing system robustness and reliability.
[2606.27757] Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
[Submitted on 26 Jun 2026]
Title:Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
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Abstract: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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.27757 [cs.AI]
(or arXiv:2606.27757v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.27757
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiajing Zhang [view email] [v1] Fri, 26 Jun 2026 06:24:33 UTC (902 KB)
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