{"ID":5937739,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T16:25:08.048777621Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04394","arxiv_id":"2607.04394","title":"MechMath Agent Team: LLM Driven Agents for Mathematical Research","abstract":"AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathematical research, which is characterized by non-linear derivation paths, rigorous logical requirements, and protracted exploration cycles, poses severe challenges for existing reasoning systems. To overcome these limitations, we present the MechMath Agent Team (MMAT), which is a large language model driven agent designed to serve as a co-pilot throughout the full cycle of mathematical research. We design a tripartite Harness Architecture that decouples system responsibilities into Control, Execution, and Augmentation planes, thereby reconciling rigorous logical control with the agility demanded by open-ended research. Building upon this framework, we instantiate three specialized agents: a Knowledge Base Manager, a Natural Language Prover, and a Formal Language Prover, all operating in a closed loop to produce formally certified mathematical proofs. We evaluate MMAT on open problems in Number Theory, Algebraic Complexity Theory, Differential Algebra, Operator Algebra, and Inequalities. Across a two-month deployment, 11 problems have been solved, demonstrating its capacity to act as a co-pilot throughout the entire research cycle. The contributions are threefold: a general decoupled Harness Architecture for multi-agent mathematical reasoning, its concrete instantiation in the MMAT system, and empirical validation on a diverse suite of open problems.","short_abstract":"AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathematical research, which is characterized by non-linear derivation paths, rigorous logical requirements, and protracted exploration cycles, poses severe challenges for ex...","url_abs":"https://arxiv.org/abs/2607.04394","url_pdf":"https://arxiv.org/pdf/2607.04394v1","authors":"[\"Yichuan Cao\",\"Ruichen Qiu\",\"Junqi Liu\",\"Jiaqi Wang\",\"Dakai Guo\",\"Ruyong Feng\",\"Lihong Zhi\",\"Xiao-Shan Gao\"]","published":"2026-07-05T16:37:40Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.SC\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}