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    <abstract>Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low diversity and suboptimal code generation. While recent work (CITATION) has introduced Monte Carlo Tree Search (MCTS) to address these issues, limitations persist in the quality and diversity of thoughts generated, as well as in the scalar value feedback mechanisms used for node selection. In this study, we introduce Introspective Monte Carlo Tree Search (I-MCTS), a novel approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. This facilitates a continuous refinement of the node in the search tree, thereby enhancing the overall decision-making process. Furthermore, we integrate a Large Language Model (LLM)-based value model to facilitate direct evaluation of each node’s solution prior to conducting comprehensive computational rollouts. A hybrid rewarding mechanism is implemented to seamlessly transition the Q-value from estimated score to actual performance scores. Applied to the various ML tasks, our approach demonstrates a 4% absolute improvement in performance compared to the strong open-source AutoML agents, showcasing its effectiveness in enhancing agentic AutoML systems. Resource available at https://github.com/jokieleung/I-MCTS</abstract>
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%0 Conference Proceedings
%T I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search
%A Liang, Zujie
%A Wei, Feng
%A Xu, Wujiang
%A Qian, Yuxi
%A Chen, Lin
%A Wu, Xinhui
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F liang-etal-2026-mcts
%X Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low diversity and suboptimal code generation. While recent work (CITATION) has introduced Monte Carlo Tree Search (MCTS) to address these issues, limitations persist in the quality and diversity of thoughts generated, as well as in the scalar value feedback mechanisms used for node selection. In this study, we introduce Introspective Monte Carlo Tree Search (I-MCTS), a novel approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. This facilitates a continuous refinement of the node in the search tree, thereby enhancing the overall decision-making process. Furthermore, we integrate a Large Language Model (LLM)-based value model to facilitate direct evaluation of each node’s solution prior to conducting comprehensive computational rollouts. A hybrid rewarding mechanism is implemented to seamlessly transition the Q-value from estimated score to actual performance scores. Applied to the various ML tasks, our approach demonstrates a 4% absolute improvement in performance compared to the strong open-source AutoML agents, showcasing its effectiveness in enhancing agentic AutoML systems. Resource available at https://github.com/jokieleung/I-MCTS
%U https://aclanthology.org/2026.findings-eacl.11/
%P 189-210

Markdown (Informal)

[I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search](https://aclanthology.org/2026.findings-eacl.11/) (Liang et al., Findings 2026)

• I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search (Liang et al., Findings 2026)

ACL

• Zujie Liang, Feng Wei, Wujiang Xu, Yuxi Qian, Lin Chen, and Xinhui Wu. 2026. I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search. In Findings of the Association for Computational Linguistics: EACL 2026, pages 189–210, Rabat, Morocco. Association for Computational Linguistics.