Toward Template-Free Explainability for Monte Carlo Tree Search
Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under uncertainty. However, interpreting asymmetric search trees that incorporate bandit-based tree traversal and simulation-based value estimation is difficult for end users based solely on raw tree statistics. While prior work requires hand-crafted formal logic constraints that must be updated when the problem changes, we present a framework that enables large language models (LLMs) to generate evidence-grounded explanations of MCTS decisions from recorded search traces in an end-to-end manner. Our framework maps natural-language questions to a ...
| Authors | Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud et al. |
|---|---|
| Primary category | cs.HC |
| Published | 15 May 2026 |
| Last updated at source | 19 May 2026 |
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