# MechMath Agent Team: LLM Driven Agents for Mathematical Research arXiv: https://arxiv.org/abs/2607.04394 ยท HTML: https://mlsanity.com/paper/2607.04394/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 48 references that are in the map. - First posted on arXiv 5 Jul 2026. ## Placed in - LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/) ## Builds on (most cited first, 27 of 27) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023) - [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024) - [Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs](https://mlsanity.com/paper/2210.12283/) (2022) - [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](https://mlsanity.com/paper/2401.08500/) (2024) - [Winning Gold at IMO 2025 with a Model-Agnostic Verification-and-Refinement Pipeline](https://mlsanity.com/paper/2507.15855/) (2025) - [Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematics](https://mlsanity.com/paper/2601.14027/) (2026) - [DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2503.07675/) (2025) - [LeanExplore: A search engine for Lean 4 declarations](https://mlsanity.com/paper/2506.11085/) (2025) - [From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration](https://mlsanity.com/paper/2603.04474/) (2026) - [QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems](https://mlsanity.com/paper/2604.24021/) (2026) - [Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues](https://mlsanity.com/paper/2407.09897/) (2024) - [Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines](https://mlsanity.com/paper/2510.07614/) (2025) - [LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving](https://mlsanity.com/paper/2605.13137/) (2026) - [Lean Finder: Semantic Search for Mathlib That Understands User Intents](https://mlsanity.com/paper/2510.15940/) (2025) - [Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2605.14892/) (2026) - [Mistake Notebook Learning: Batch-Clustered Failures for Training-Free Agent Adaptation](https://mlsanity.com/paper/2512.11485/) (2025) - [Essentially Optimal Sparse Polynomial Multiplication](https://mlsanity.com/paper/2001.11959/) (2020) - [A Makespan and Energy-Aware Scheduling Algorithm for Workflows under Reliability Constraint on a Multiprocessor Platform](https://mlsanity.com/paper/2212.09274/) (2022) - [A Finite Certificate for the Positive n=9 Vasc Inequality](https://mlsanity.com/paper/2606.06136/) (2026) - [A Greatest Common Divisor Criterion of Certain Binomial Coefficients](https://mlsanity.com/paper/2606.22997/) (2026) - [Deterministic Polynomial-time Exact-root Computation for Sparse Polynomials with Bounded Total Degree](https://mlsanity.com/paper/2607.02364/) (2026) ## Built on by (most cited first, 5 of 5) - [VALG: An Agentic System for ML Theory Research](https://mlsanity.com/paper/2608.13060/) (2026) - [MECA: A Mechanism-Centered Agent for Constructing Well-Specified and Valuable Mathematical Conjectures](https://mlsanity.com/paper/2607.27709/) (2026) - [Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science](https://mlsanity.com/paper/2609.15983/) (2026) - [Andy: A Mathematical Agent for Rigorous Proof and Autonomous Research](https://mlsanity.com/paper/2608.15052/) (2026) - [Euler: Towards Human-Centered Multi-Agent Mathematical Discovery](https://mlsanity.com/paper/2610.19864/) (2026) ## Abstract (arXiv) 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.