With the development of artificial intelligence (AI), large language models (LLM) are widely used in many fields. However, the reasoning ability of LLM is still very limited when it comes to mathematical reasoning. Mathematics plays an important role in all aspects of human society and is a technical guarantee in the fields of healthcare, transport and aerospace, for this reason, the development of AI big language models in the field of mathematics has great potential significance. To improve the mathematical reasoning ability of large language models, we proposed an agent framework for learning to solve mathematical problems based on inductive reasoning. By emulating the human learning process of generalization of learned information and effective application of previous knowledge in new reasoning tasks, this framework has great performance in the mathematical reasoning process. It improves global accuracy over the baseline method (chain-of-thought) by 20.96% and solves 17.54% of the mathematical problems that the baseline cannot solve. Benefiting from the efficient RETRIEVAL method, our model improves the ability of large language models to efficiently use external knowledge, i.e., the mathematical computation of the model can be based on written procedures. In education, our model can be used as a personalised learning aid, thus reducing the inequality of educational resources.
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection
Squirrel Ai Learning；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；University of Illinois at Chicago（伊利诺伊大学香槟分校）
Code2Math: Can Your Code Agent Evolve Math Problems Through Exploration?
Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；Nanjing Tech University（南京工业大学）；Shanghai Jiao Tong University（上海交通大学）；University of Michigan（密歇根大学）；Independent Researcher（独立研究者）
Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Research Institute for Mathematical Sciences, Kyoto University（京都大学数理解析研究所）；School of Mathematics, Tianjin University（天津大学数学学院）；Zhongguancun Academy（中关村科学院）；Department of Mathematics, Stanford University（斯坦福大学数学系）；Westlake Institute for Advanced Study, Westlake University（西湖大学西湖高等研究院）；School of Mathematical Sciences, Key Laboratory of Intelligent Computing and Applications (Ministry of Education), Tongji University（同济大学数学科学学院（教育部智能计算与应用重点实验室））；Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory, Peking University（北京大学北京国际数学研究中心与新基石科学实验室）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Center for Intelligent Computing, Great Bay Institute for Advanced Study, Great Bay University（大湾区大学高等研究院智能计算中心）
Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics
University College London（伦敦大学学院）；Advanced Comprehensive Research Organization, Teikyo University（帝京大学先进综合研究机构）；Preferred Networks, Inc.（Preferred Networks公司）；RedBud AI, LLC（RedBud AI有限责任公司）；Lutheran University of Brazil（巴西路德大学）
Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving
Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA（智能系统研究所，密苏里大学）；Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA（电气与计算机工程系，密苏里大学）；AI4STEM Education Center, University of Georgia, Athens, GA, USA（AI4STEM教育中心，佐治亚大学）；National GENIUS Center, Athens, GA, USA（国家GENIUS中心）；Department of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, GA, USA（数学、科学与社会科学教育系，佐治亚大学）；Faculty of Education, The University of Hong Kong, Hong Kong, PR China（教育学院，香港大学）；Educational Testing Service, Princeton, NJ, USA（教育测评服务）；Department of Applied Social Sciences, Hong Kong Polytechnic University, Hong Kong, PR China（应用社会科学系，香港理工大学）；Department of Psychology, University of Memphis, Memphis, TN, USA（心理学系，密苏里大学）
该研究使用 GPT-4o 在 MATH 数据集上比较双智能体数学解题的四种沟通模式，发现同伴协作准确率最高，并表明有效对话行为是提升 AI 教育协作解题的关键。
AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation
King’s College London（伦敦国王学院）；Hong Kong Baptist University（香港 Baptist 大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；ByteDance Seed（字节跳动种子）；City University of Hong Kong（香港城市大学）；Xi’an Jiaotong University（西安交通大学）
Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；MMLab；The Chinese University of Hong Kong（香港中文大学）；ICMAT Spanish National Research Council（西班牙国家科研 council）；The High School Affiliated to Renmin University of China（中国人民大学附属高中）；Ren Hui Academy of Beijing（北京润辉学院）
Bolzano: Case Studies in LLM-Assisted Mathematical Research
Computer Science Institute, Charles University（查尔斯大学计算机科学研究所）；Institute of Mathematics, Czech Academy of Sciences（捷克科学院数学研究所）；Institute of Formal and Applied Linguistics, Charles University（查尔斯大学形式与应用语言学研究所）；Department of Applied Mathematics, Charles University（查尔斯大学应用数学系）
Hey AI, Can You Solve Complex Tasks by Talking to Agents?
Allen Institute for AI（艾伦人工智能研究所）
为每个复杂任务从头开始训练巨型模型是资源和数据效率低下的。为了帮助开发能够利用现有系统的模型，我们提出了一个新的挑战：通过用自然语言与现有代理（或模型）进行通信来学习解决复杂任务。我们设计了一个综合基准，CommaQA，三个复杂的推理任务（显式，隐式，数值），旨在解决与现有的QA代理通信。例如，使用文本和表格QA代理来回答诸如“美国谁的标枪投掷最长？".我们表明，黑盒模型努力从头开始学习这个任务（准确率低于50%），即使访问每个代理的知识和黄金事实监督。相比之下，学习与代理沟通的模型优于黑盒模型，在给予黄金分解监督时达到100%的分数。然而，我们发现，通过与现有代理\emph{without relying on any auxiliary supervision or data}通信来学习解决复杂任务的挑战仍然非常难以捉摸。我们发布了CommaQA，以及一个组合泛化测试分割，以推进这一方向的研究。数据集和代码可在https://github.com/allenai/commaqa获得。
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
Beijing University of Posts and Telecommunications（北京邮电大学）；Huazhong University of Science and Technology（华中科技大学）；University of Science and Technology of China（中国科学技术大学）；Dalian University of Technology（大连理工大学）；Renmin University of China（中国人民大学）；Shanghai Jiao Tong University（上海交通大学）；Beihang University（北京航空航天大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peking University（北京大学）；Nanjing University（南京大学）
