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Large Language Models often struggle with complex planning tasks that require exploration, backtracking, and self-correction. Once an LLM commits to an early mistake, its linear chain-of-thought reasoning makes recovery difficult. While search methods like Monte Carlo Tree Search (MCTS) offer a way to explore alternatives, they typically rely on sparse rewards and fail to fully exploit the semantic strengths of language models.

In this episode, we dive into SPIRAL (Symbolic LLM Planning via Grounded and Reflective Search), a new framework that fundamentally rethinks how planning and search interact in LLM-based agents. Instead of treating MCTS as a brute-force optimizer, SPIRAL embeds a cognitive architecture of three specialized LLM roles directly into the search loop:

• A Planner proposes creative next actions,
• A Simulator grounds those actions by predicting realistic outcomes, and
• A Critic reflects on the results to provide dense, informative reward signals.

This planner–simulator–critic loop transforms search into a guided, self-correcting reasoning process, allowing agents to recover from mistakes, evaluate alternatives more effectively, and plan with far greater robustness.

Paper link: https://arxiv.org/pdf/2512.23167

Repo: https://github.com/IBM/SPIRAL