# Toward Template-Free Explainability for Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/2605.16524 · HTML: https://mlsanity.com/paper/2605.16524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations](https://mlsanity.com/paper/2002.01080/) (2020)
- [Hierarchical Planning for Resource Allocation in Emergency Response Systems](https://mlsanity.com/paper/2012.13300/) (2020)
- [Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results](https://mlsanity.com/paper/2307.13453/) (2023)
- [On the Modeling Capabilities of Large Language Models for Sequential Decision Making](https://mlsanity.com/paper/2410.05656/) (2024)
- [Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic](https://mlsanity.com/paper/2407.10820/) (2024)
- [Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit](https://mlsanity.com/paper/2204.11992/) (2022)
- [Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes](https://mlsanity.com/paper/2401.01841/) (2024)
- [Digital Guardians: The Past and The Future of Cyber-Physical Resilience](https://mlsanity.com/paper/2604.14360/) (2026)

## Abstract (arXiv)

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 structured set of intent categories, determines whether the existing tree contains sufficient evidence, triggers targeted expansion when needed, and generates explanations using tree statistics such as visit counts, value estimates, and risk information. Experimental results provide the first evidence that LLMs can serve as end-to-end explainers for probabilistic search, without requiring intermediate formal representations.