# M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMs

arXiv: https://arxiv.org/abs/2604.14687 · HTML: https://mlsanity.com/paper/2604.14687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 6 of 6)

- [LLMs for Explainable AI: A Comprehensive Survey](https://mlsanity.com/paper/2504.00125/) (2025)
- [Distal Explanations for Model-free Explainable Reinforcement Learning](https://mlsanity.com/paper/2001.10284/) (2020)
- [A Survey on Explainable Deep Reinforcement Learning](https://mlsanity.com/paper/2502.06869/) (2025)
- [Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic](https://mlsanity.com/paper/2407.10820/) (2024)
- [Process Discovery using Inductive Miner and Decomposition](https://mlsanity.com/paper/1610.07989/) (2016)
- [Structure and Reduction of MCTS for Explainable-AI](https://mlsanity.com/paper/2408.05488/) (2024)

## Abstract (arXiv)

Monte-Carlo Tree Search (MCTS) is a fundamental sampling-based search algorithm widely used for online planning in sequential decision-making domains. Despite its success in driving recent advances in artificial intelligence, understanding the behavior of MCTS agents remains a challenge for both developers and users. This difficulty stems from the complex search trees produced through the simulation of numerous future states and their intricate relationships. A known weakness of standard MCTS is its reliance on highly selective tree construction, which may lead to the omission of crucial moves and a vulnerability to tactical traps. To resolve this, we incorporate shallow, full-width Minimax search into the rollout phase of multi-agent MCTS to enhance strategic depth. Furthermore, to demystify the resulting decision-making logic, we introduce textsfM2-PALE (MCTS--Minimax Process-Aided Linguistic Explanations). This framework employs process mining techniques, specifically the Alpha Miner, iDHM, and Inductive Miner algorithms, to extract underlying behavioral workflows from agent execution traces. These process models are then synthesized by LLMs to generate human-readable causal and distal explanations. We demonstrate the efficacy of our approach in a small-scale checkers environment, establishing a scalable foundation for interpreting hybrid agents in increasingly complex strategic domains.