@inproceedings{xu-etal-2025-aristotle,
title = "Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework",
author = "Xu, Jundong and
Fei, Hao and
Luo, Meng and
Liu, Qian and
Pan, Liangming and
Wang, William Yang and
Nakov, Preslav and
Lee, Mong-Li and
Hsu, Wynne",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.153/",
doi = "10.18653/v1/2025.acl-long.153",
pages = "3052--3075",
ISBN = "979-8-89176-251-0",
abstract = "In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks. However, when it comes to logical reasoning tasks, significant challenges remain in both efficacy and efficiency. This is rooted in the fact that these systems fail to fully leverage the inherent structure of logical tasks throughout the reasoning processes, including decomposition, search, and resolution. To address this, this paper proposes a logic-complete reasoning framework, Aristotle. The framework consists of three key components: Logical Decomposer, Logical Search Router, and Logical Resolver, in which symbolic expressions and logical rules are comprehensively integrated into the entire reasoning process, significantly alleviating the bottlenecks of logical reasoning, i.e., reducing sub-task complexity, minimizing search errors, and resolving logical contradictions. Experimental results demonstrate that Aristotle consistently outperforms state-of-the-art reasoning frameworks in both accuracy and efficiency, particularly excelling in complex logical reasoning scenarios."
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<abstract>In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks. However, when it comes to logical reasoning tasks, significant challenges remain in both efficacy and efficiency. This is rooted in the fact that these systems fail to fully leverage the inherent structure of logical tasks throughout the reasoning processes, including decomposition, search, and resolution. To address this, this paper proposes a logic-complete reasoning framework, Aristotle. The framework consists of three key components: Logical Decomposer, Logical Search Router, and Logical Resolver, in which symbolic expressions and logical rules are comprehensively integrated into the entire reasoning process, significantly alleviating the bottlenecks of logical reasoning, i.e., reducing sub-task complexity, minimizing search errors, and resolving logical contradictions. Experimental results demonstrate that Aristotle consistently outperforms state-of-the-art reasoning frameworks in both accuracy and efficiency, particularly excelling in complex logical reasoning scenarios.</abstract>
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%0 Conference Proceedings
%T Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework
%A Xu, Jundong
%A Fei, Hao
%A Luo, Meng
%A Liu, Qian
%A Pan, Liangming
%A Wang, William Yang
%A Nakov, Preslav
%A Lee, Mong-Li
%A Hsu, Wynne
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F xu-etal-2025-aristotle
%X In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks. However, when it comes to logical reasoning tasks, significant challenges remain in both efficacy and efficiency. This is rooted in the fact that these systems fail to fully leverage the inherent structure of logical tasks throughout the reasoning processes, including decomposition, search, and resolution. To address this, this paper proposes a logic-complete reasoning framework, Aristotle. The framework consists of three key components: Logical Decomposer, Logical Search Router, and Logical Resolver, in which symbolic expressions and logical rules are comprehensively integrated into the entire reasoning process, significantly alleviating the bottlenecks of logical reasoning, i.e., reducing sub-task complexity, minimizing search errors, and resolving logical contradictions. Experimental results demonstrate that Aristotle consistently outperforms state-of-the-art reasoning frameworks in both accuracy and efficiency, particularly excelling in complex logical reasoning scenarios.
%R 10.18653/v1/2025.acl-long.153
%U https://aclanthology.org/2025.acl-long.153/
%U https://doi.org/10.18653/v1/2025.acl-long.153
%P 3052-3075
Markdown (Informal)
[Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework](https://aclanthology.org/2025.acl-long.153/) (Xu et al., ACL 2025)
ACL
- Jundong Xu, Hao Fei, Meng Luo, Qian Liu, Liangming Pan, William Yang Wang, Preslav Nakov, Mong-Li Lee, and Wynne Hsu. 2025. Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3052–3075, Vienna, Austria. Association for Computational Linguistics.