{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T22:04:53Z","timestamp":1779487493330,"version":"3.53.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.neucom.2026.133793","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:08:07Z","timestamp":1777590487000},"page":"133793","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["VGRP-MCTS: Enhancing rule-guided question answering via verifier-guided rule planning with monte carlo tree search"],"prefix":"10.1016","volume":"690","author":[{"given":"Xiaoguang","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaofan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9474-7377","authenticated-orcid":false,"given":"Yifan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3986-9685","authenticated-orcid":false,"given":"Junjie","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4260-4550","authenticated-orcid":false,"given":"Yuting","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyan","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133793_bib0005","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s40537-023-00802-8","article-title":"Exploring the state of the art in legal QA systems","volume":"10","author":"Abdallah","year":"2023","journal-title":"J. Big Data"},{"key":"10.1016\/j.neucom.2026.133793_bib0010","series-title":"Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems","doi-asserted-by":"crossref","first-page":"50","DOI":"10.65109\/WJJV5555","article-title":"Scmrag: self-corrective multihop retrieval augmented generation system for LLM agents","author":"Agrawal","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0015","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","first-page":"743","article-title":"PolicyQA: a reading comprehension dataset for privacy policies","author":"Ahmad","year":"2020"},{"key":"10.1016\/j.neucom.2026.133793_bib0020","series-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online","first-page":"268","article-title":"ETC: encoding long and structured inputs in transformers","author":"Ainslie","year":"2020"},{"key":"10.1016\/j.neucom.2026.133793_bib0025","series-title":"The Twelfth International Conference on Learning Representations","article-title":"Self-RAG: learning to retrieve, generate, and critique through self-reflection","author":"Asai","year":"2024"},{"key":"10.1016\/j.neucom.2026.133793_bib0030","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"17682","article-title":"Graph of thoughts: solving elaborate problems with large language models","volume":"vol. 38","author":"Besta","year":"2024"},{"key":"10.1016\/j.neucom.2026.133793_bib0035","author":"Brown"},{"key":"10.1016\/j.neucom.2026.133793_bib0040","series-title":"Findings of the Association for Computational Linguistics: ACL 2025","first-page":"7433","article-title":"Boosting policy and process reward models with monte carlo tree search in open-domain QA","author":"Chan","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0045","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","first-page":"216","article-title":"Monte-carlo tree search: a new framework for game AI","volume":"vol. 4","author":"Chaslot","year":"2008"},{"key":"10.1016\/j.neucom.2026.133793_bib0050","series-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","first-page":"3697","article-title":"FinQA: a dataset of numerical reasoning over financial data","author":"Chen","year":"2021"},{"key":"10.1016\/j.neucom.2026.133793_bib0055","author":"Chen"},{"key":"10.1016\/j.neucom.2026.133793_bib0060","series-title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, International Joint Conferences on Artificial Intelligence Organization","first-page":"3882","article-title":"Transformers as soft reasoners over language","author":"Clark","year":"2020"},{"key":"10.1016\/j.neucom.2026.133793_bib0065","series-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online","first-page":"8823","article-title":"Hierarchical graph network for multi-hop question answering","author":"Fang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133793_bib0070","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online","first-page":"946","article-title":"Injecting numerical reasoning skills into language models","author":"Geva","year":"2020"},{"key":"10.1016\/j.neucom.2026.133793_bib0075","series-title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","first-page":"10530","article-title":"DSG-MCTS: a dynamic strategy-guided monte carlo tree search for diversified reasoning in large language models","author":"Ha","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0080","series-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"2030","article-title":"Embedding logical queries on knowledge graphs","author":"Hamilton","year":"2018"},{"key":"10.1016\/j.neucom.2026.133793_bib0085","series-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","article-title":"Training compute-optimal large language models","author":"Hoffmann","year":"2022"},{"key":"10.1016\/j.neucom.2026.133793_bib0090","series-title":"Proceedings of the 41st International Conference on Machine Learning","article-title":"Case-based or rule-based: how do transformers do the math?","author":"Hu","year":"2024"},{"key":"10.1016\/j.neucom.2026.133793_bib0095","author":"Hu"},{"key":"10.1016\/j.neucom.2026.133793_bib0100","series-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics","first-page":"2410","article-title":"Harnessing deep neural networks with logic rules","author":"Hu","year":"2016"},{"key":"10.1016\/j.neucom.2026.133793_bib0105","series-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","first-page":"2567","article-title":"PubMedQA: a dataset for biomedical research question answering","author":"Jin","year":"2019"},{"key":"10.1016\/j.neucom.2026.133793_bib0110","author":"Lambert"},{"key":"10.1016\/j.neucom.2026.133793_bib0115","author":"Lee"},{"key":"10.1016\/j.neucom.2026.133793_bib0120","author":"Li"},{"key":"10.1016\/j.neucom.2026.133793_bib0125","series-title":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"From easy to hard: two-stage selector and reader for multi-hop question answering","author":"Li","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0130","doi-asserted-by":"crossref","DOI":"10.1007\/s10489-025-07044-6","article-title":"CMCTS: a constrained monte carlo tree search framework for mathematical reasoning in large language models","volume":"56","author":"Lin","year":"2026","journal-title":"Appl. Intell."},{"key":"10.1016\/j.neucom.2026.133793_bib0135","series-title":"2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC)","first-page":"86","article-title":"A knowledge-based question-answering method for military critical information under limited corpus","author":"Liu","year":"2021"},{"key":"10.1016\/j.neucom.2026.133793_bib0140","author":"Luo"},{"key":"10.1016\/j.neucom.2026.133793_bib0145","series-title":"Advances in Neural Information Processing Systems","first-page":"46534","article-title":"Self-refine: iterative refinement with self-feedback","author":"Madaan","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0150","author":"Maram"},{"key":"10.1016\/j.neucom.2026.133793_bib0155","author":"Nahid"},{"key":"10.1016\/j.neucom.2026.133793_bib0160","doi-asserted-by":"crossref","first-page":"717","DOI":"10.13052\/jwe1540-9589.1785","article-title":"A review of question answering systems","volume":"17","author":"Ojokoh","year":"2018","journal-title":"J. Web Eng."},{"key":"10.1016\/j.neucom.2026.133793_bib0165","series-title":"The Thirteenth International Conference on Learning Representations","article-title":"Chain-of-action: faithful and multimodal question answering through large language models","author":"Pan","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0170","series-title":"Probabilistic Logic Neural Networks for Reasoning","author":"Qu","year":"2019"},{"key":"10.1016\/j.neucom.2026.133793_bib0175","first-page":"7346","article-title":"The effect of sampling temperature on problem solving in large language models","author":"Renze","year":"2024"},{"key":"10.1016\/j.neucom.2026.133793_bib0180","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10472-011-9258-6","article-title":"Multi-armed bandits with episode context","volume":"61","author":"Rosin","year":"2011","journal-title":"Ann. Math. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133793_bib0185","series-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","first-page":"1460","article-title":"RuleBERT: teaching soft rules to pre-trained language models","author":"Saeed","year":"2021"},{"key":"10.1016\/j.neucom.2026.133793_bib0190","series-title":"Advances in Neural Information Processing Systems","first-page":"8634","article-title":"Reflexion: language agents with verbal reinforcement learning","author":"Shinn","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0195","series-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"3627","article-title":"ConditionalQA: a complex reading comprehension dataset with conditional answers","author":"Sun","year":"2022"},{"key":"10.1016\/j.neucom.2026.133793_bib0200","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics","first-page":"10014","article-title":"Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions","author":"Trivedi","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0205","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"9073","article-title":"Select, answer and explain: interpretable multi-hop reading comprehension over multiple documents","author":"Tu","year":"2020"},{"key":"10.1016\/j.neucom.2026.133793_bib0210","series-title":"International Conference on Learning Representations","first-page":"33944","article-title":"Mixture-of-agents enhances large language model capabilities","author":"Wang","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0215","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Self-consistency improves chain of thought reasoning in language models","author":"Wang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0220","author":"Wang"},{"key":"10.1016\/j.neucom.2026.133793_bib0225","series-title":"Advances in Neural Information Processing Systems","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","author":"Wei","year":"2022"},{"key":"10.1016\/j.neucom.2026.133793_bib0230","series-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","article-title":"Chain-of-thought prompting elicits reasoning in large language models","author":"Wei","year":"2022"},{"key":"10.1016\/j.neucom.2026.133793_bib0235","series-title":"Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023), the Association for Computational Linguistics and Chinese Language Processing (ACLCLP)","first-page":"215","article-title":"KNOT-MCTS: an effective approach to addressing hallucinations in generative language modeling for question answering","author":"Wu","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0240","series-title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","first-page":"4067","article-title":"ReAgent: reversible multi-agent reasoning for knowledge-enhanced multi-hop QA","author":"Xinjie","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0245","author":"Xiong"},{"key":"10.1016\/j.neucom.2026.133793_bib0250","series-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","first-page":"2369","article-title":"HotpotQA: a dataset for diverse, explainable multi-hop question answering","author":"Yang","year":"2018"},{"key":"10.1016\/j.neucom.2026.133793_bib0255","series-title":"Advances in Neural Information Processing Systems","first-page":"11809","article-title":"Tree of thoughts: deliberate problem solving with large language models","author":"Yao","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0260","series-title":"Publisher Copyright: \u00a9 2023 11th International Conference on Learning Representations, ICLR 2023. All Rights Reserved.; 11th International Conference on Learning Representations, ICLR 2023","article-title":"React: synergizing reasoning and acting in language models","author":"Yao","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0265","series-title":"Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)","first-page":"1718","article-title":"End-to-end beam retrieval for multi-hop question answering","author":"Zhang","year":"2024"},{"key":"10.1016\/j.neucom.2026.133793_bib0270","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11703","article-title":"Dkplm: decomposable knowledge-enhanced pre-trained language model for natural language understanding","volume":"vol. 36","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2026.133793_bib0275","series-title":"International Conference on Learning Representations","first-page":"37697","article-title":"Ruag: learned-rule-augmented generation for large language models","author":"Zhang","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0280","series-title":"Proceedings of the 31st International Conference on Computational Linguistics, Association for Computational Linguistics, Abu Dhabi, UAE","first-page":"8399","article-title":"Rule-KBQA: rule-guided reasoning for complex knowledge base question answering with large language models","author":"Zhang","year":"2025"},{"key":"10.1016\/j.neucom.2026.133793_bib0285","series-title":"Proceedings of the 22nd Chinese National Conference on Computational Linguistics","first-page":"611","article-title":"Rethinking label smoothing on multi-hop question answering","author":"Zhangyue","year":"2023"},{"key":"10.1016\/j.neucom.2026.133793_bib0290","author":"Zhou"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226011902?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226011902?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T21:24:06Z","timestamp":1779485046000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226011902"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":58,"alternative-id":["S0925231226011902"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133793","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"VGRP-MCTS: Enhancing rule-guided question answering via verifier-guided rule planning with monte carlo tree search","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133793","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133793"}}