Research Paper#LLM Planning, Search Algorithms, Cognitive Architecture🔬 ResearchAnalyzed: Jan 3, 2026 16:12
SPIRAL: LLM Planning with Grounded Search
Analysis
This paper introduces SPIRAL, a novel framework for LLM planning that integrates a cognitive architecture within a Monte Carlo Tree Search (MCTS) loop. It addresses the limitations of LLMs in complex planning tasks by incorporating a Planner, Simulator, and Critic to guide the search process. The key contribution is the synergy between these agents, transforming MCTS into a guided, self-correcting reasoning process. The paper demonstrates significant performance improvements over existing methods on benchmark datasets, highlighting the effectiveness of the proposed approach.
Key Takeaways
- •SPIRAL is a novel framework for LLM planning that integrates a cognitive architecture within an MCTS loop.
- •It uses a Planner, Simulator, and Critic to guide the search process.
- •SPIRAL significantly outperforms existing methods on benchmark datasets.
- •The approach demonstrates superior token efficiency.
Reference
“SPIRAL achieves 83.6% overall accuracy on DailyLifeAPIs, an improvement of over 16 percentage points against the next-best search framework.”