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

LLMs struggle with complex planning tasks: Large language models frequently produce infeasible or incorrect solutions when asked to handle long-horizon decision-making problems, indicating a significant reliability gap that needs addressing before deployment in high-stakes applications.

Symbolic feedback improves AI decision-making: The framework uses a "symbolic verifier" to identify errors and convert them into corrective instructions, demonstrating that structured feedback mechanisms can guide AI systems toward better solutions—a practical approach to making AI more reliable.

Self-correction is trainable: By implementing iterative refinement where LLMs learn from their mistakes through natural language feedback, the research shows that AI systems can improve their own outputs, potentially reducing the need for constant human oversight.

Verification mechanisms are essential for trustworthy AI: The inclusion of a "plan recognizer" to check whether goals are actually achievable before execution highlights why independent verification systems are critical for responsible AI deployment—catching errors before they cause real-world harm.

Measurable improvements in feasibility and correctness: The empirical results demonstrate consistent improvements in both the feasibility and correctness of planning tasks, providing concrete evidence that deliberate design choices can meaningfully enhance AI system reliability rather than relying on hope or scale alone.