{"slug": "towards-reliable-and-robust-llm-planning-symbolic-feedback-driven-iterative-self", "title": "Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework", "summary": "Researchers propose a symbolic feedback-driven iterative self-refinement framework to improve the robustness and reliability of large language models in long-horizon planning tasks. The framework uses natural language prompting, a symbolic verifier, and a plan recognizer to enhance feasibility and correctness, demonstrating consistent improvements in empirical results.", "body_md": "arXiv:2606.27757v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/towards-reliable-and-robust-llm-planning-symbolic-feedback-driven-iterative-self", "canonical_source": "https://arxiv.org/abs/2606.27757", "published_at": "2026-06-29 04:00:00+00:00", "updated_at": "2026-06-29 04:11:07.380441+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-research", "ai-agents"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/towards-reliable-and-robust-llm-planning-symbolic-feedback-driven-iterative-self", "markdown": "https://wpnews.pro/news/towards-reliable-and-robust-llm-planning-symbolic-feedback-driven-iterative-self.md", "text": "https://wpnews.pro/news/towards-reliable-and-robust-llm-planning-symbolic-feedback-driven-iterative-self.txt", "jsonld": "https://wpnews.pro/news/towards-reliable-and-robust-llm-planning-symbolic-feedback-driven-iterative-self.jsonld"}}