LLM Framework Explains Monte Carlo Tree Search Decisions
Researchers have come up with a new framework that uses large language models (LLMs) to help explain how the Monte Carlo Tree Search (MCTS) algorithm makes decisions. MCTS is a method used for making choices when there's uncertainty. This new system can take questions in everyday language and sort them into clear categories. It checks the evidence in the search tree and expands it when needed. The explanations it generates rely on various tree statistics, like visit counts and risk data. This approach eliminates the need for specific logic rules that need constant updates, offering a flexible solution for understanding complex search trees using bandit methods and simulation for value assessment.
Key facts
- Framework enables LLMs to generate evidence-grounded explanations of MCTS decisions from recorded search traces.
- Maps natural-language questions to a structured set of intent categories.
- Determines whether existing tree contains sufficient evidence.
- Triggers targeted expansion when needed.
- Uses tree statistics such as visit counts, value estimates, and risk information.
- Eliminates need for hand-crafted formal logic constraints.
- Addresses difficulty of interpreting asymmetric search trees for end users.
- Published on arXiv with ID 2605.16524.
Entities
Institutions
- arXiv