@inproceedings{li-etal-2026-llm,
title = "{LLM} Inductive Reasoning Through Multi-Agent Enhanced {M}onte {C}arlo Tree Search",
author = "Li, Xiang and
Zhou, Yucheng and
Wei, Xiangzhi and
Shi, Zesheng and
Wan, Haiyuan and
Yifan, Gong and
Liu, Fangming and
Li, Jing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1178/",
pages = "23548--23562",
ISBN = "979-8-89176-395-1",
abstract = "Existing methods for enhancing the inductive reasoning of large language models (LLMs) at test-time typically depend on iterative self-refinement of hypotheses, which lacks explicit optimization guidance and effective error correction. This often results in superficial rewording and the accumulation of errors. To overcome these limitations, we propose MATSIR, a plug-and-play test-time framework integrating Multi-Agent coordination with Monte Carlo Tree Search to improve Inductive Reasoning. MATSIR incorporates a dual-reward mechanism that provides explicit refinement signals, promoting logically coherent and semantically enriched hypotheses rather than mere rephrasing. Furthermore, it enables trajectory-level error correction through backtracking and pruning, allowing the system to recover from erroneous intermediate hypotheses. Experiments on five benchmarks across four LLMs show that MATSIR consistently outperforms previous best methods, yielding the highest average improvement of +4.9{\%} on QWQ-32B and all-round improvement on Deepseek-V3. Our findings highlight that explicit guided search with built-in error correction is essential for advancing inductive reasoning in LLMs. Code is available at https://github.com/SolarWindRider/MATSIR"
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<abstract>Existing methods for enhancing the inductive reasoning of large language models (LLMs) at test-time typically depend on iterative self-refinement of hypotheses, which lacks explicit optimization guidance and effective error correction. This often results in superficial rewording and the accumulation of errors. To overcome these limitations, we propose MATSIR, a plug-and-play test-time framework integrating Multi-Agent coordination with Monte Carlo Tree Search to improve Inductive Reasoning. MATSIR incorporates a dual-reward mechanism that provides explicit refinement signals, promoting logically coherent and semantically enriched hypotheses rather than mere rephrasing. Furthermore, it enables trajectory-level error correction through backtracking and pruning, allowing the system to recover from erroneous intermediate hypotheses. Experiments on five benchmarks across four LLMs show that MATSIR consistently outperforms previous best methods, yielding the highest average improvement of +4.9% on QWQ-32B and all-round improvement on Deepseek-V3. Our findings highlight that explicit guided search with built-in error correction is essential for advancing inductive reasoning in LLMs. Code is available at https://github.com/SolarWindRider/MATSIR</abstract>
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%0 Conference Proceedings
%T LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search
%A Li, Xiang
%A Zhou, Yucheng
%A Wei, Xiangzhi
%A Shi, Zesheng
%A Wan, Haiyuan
%A Yifan, Gong
%A Liu, Fangming
%A Li, Jing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-llm
%X Existing methods for enhancing the inductive reasoning of large language models (LLMs) at test-time typically depend on iterative self-refinement of hypotheses, which lacks explicit optimization guidance and effective error correction. This often results in superficial rewording and the accumulation of errors. To overcome these limitations, we propose MATSIR, a plug-and-play test-time framework integrating Multi-Agent coordination with Monte Carlo Tree Search to improve Inductive Reasoning. MATSIR incorporates a dual-reward mechanism that provides explicit refinement signals, promoting logically coherent and semantically enriched hypotheses rather than mere rephrasing. Furthermore, it enables trajectory-level error correction through backtracking and pruning, allowing the system to recover from erroneous intermediate hypotheses. Experiments on five benchmarks across four LLMs show that MATSIR consistently outperforms previous best methods, yielding the highest average improvement of +4.9% on QWQ-32B and all-round improvement on Deepseek-V3. Our findings highlight that explicit guided search with built-in error correction is essential for advancing inductive reasoning in LLMs. Code is available at https://github.com/SolarWindRider/MATSIR
%U https://aclanthology.org/2026.findings-acl.1178/
%P 23548-23562
Markdown (Informal)
[LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search](https://aclanthology.org/2026.findings-acl.1178/) (Li et al., Findings 2026)
ACL
- Xiang Li, Yucheng Zhou, Xiangzhi Wei, Zesheng Shi, Haiyuan Wan, Gong Yifan, Fangming Liu, and Jing Li. 2026. LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23548–23562, San Diego, California, United States. Association for Computational Linguistics.