Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
arXiv:2606.27757v1 Announce Type: new 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.
The rapid advancement and widespread deployment of LLMs highlight current limitations in robustness and reliability, making solutions for planning critical for further integration into complex systems.
Improving LLM planning directly addresses a core barrier to autonomous AI agent development, increasing their utility and trustworthiness in real-world, long-horizon tasks.
The introduction of symbolic feedback and iterative self-refinement promises more reliable and robust LLM outputs for complex planning, potentially accelerating the deployment of advanced AI agents.
- · AI developers
- · Robotics companies
- · Automation sector
- · LLM researchers
- · LLMs without advanced planning capabilities
- · Manual oversight in complex decision systems
More sophisticated and reliable AI agents become deployable across various industries.
Reduced human intervention in complex operational planning and task execution, increasing efficiency.
Accelerated development of autonomous systems that can manage and adapt to unforeseen circumstances in dynamic environments.
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Read at arXiv cs.AI