[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84730-en":3,"doc-seo-84730-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84730,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","MechMath Agent Team LLM Driven Agents for Mathematical Research","AI reasoning is increasingly shaped by large language model successes, yet mathematical research demands non-linear derivations, strict logical rigor, and long exploration cycles that many systems cannot handle well. MechMath Agent Team (MMAT) presents an LLM-driven co-pilot covering the full research loop. It uses a tripartite Harness Architecture—Control, Execution, and Augmentation planes—to combine deterministic logical control with open-ended agility. Three closed-loop agents (KB-Manager, NL-Prover, FL-Prover) produce formally certified proofs and are evaluated on multiple number theory and algebraic tasks.","arXiv :2607 .04394v 1 [ cs .AI ] 5 Jul 2026  \nMechMath Agent Team: LLM Driven Agents for Mathematical Research  \nYichuan Cao12,*, Ruichen Qiu13,*, Junqi Liu12, Jiaqi Wang12, Dakai Guo1, Ruyong Feng12, Lihong Zhi12, Xiao-Shan Gao12,+  \nAbstract  \nAI 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.  \n1 Introduction  \nAI reasoning has been a constitutive pillar of artificial intelligence since its inception at the 1956 Dartmouth Summer Research Project [31] . In recent years, it has re-emerged as a central focus, driven overwhelmingly by the proliferation of large language models (LLMs) [40] . Through prompting strategies such as chain-of-thought [48], LLMs exhibit emergent multi-step inference capabilities. Additionally, LLM-based agents have expanded the scope of reasoning by integrating external tools and interacting with dynamic environments, supporting more deliberate and plan-oriented problem solving [21, 47] . Together, these developments have raised new questions about the nature and evaluation of reasoning, cementing it as oneof the most dynamic frontiers in contemporary AI.  \nDespite these advances, directly applying such reasoning capabilities to mathematical research remains particularly challenging, given its non-linearity derivation trajectories, strict logical requirements, and protracted exploration cycles. Conventional linear multi-agent pipelines frequently struggle with these demands due to their rigid, hard-coded workflows and vulnerability to error propagation [3, 35] . To address these challenges, we depart from traditional frameworks and introduce the MechMath Agent Team (MMAT), a multi-agent system built upon a disciplined, structurally robust Harness Architecture. By flexibly mounting various sub-agents and toolchains, MMAT coordinates multiple specialized large language models (LLMs) to work collaboratively while maintaining deterministic system states and traceable execution histories. In particular, it is designed to be agent-agnostic, making it easy to integrate with current  \n1State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, CAS 2School of Mathematical Sciences, University of Chinese Academy of Sciences  \n∗ Equal contribution.  \n3School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences + Corresponding author.  \nGeneral Harness  \n|  | ... |\n| --- | --- |\n| ORCH ING RES |  |\n|  | ... |\n| ORCH EXPL SKE |  |\n|  | ... |\n| ORCH F-RE F-GE |  |\n\nSpecific Subagents  \nSpecific Tools  \n ...  \n ...","cbCaihRQGgVbYDBS","https://ap.wps.com/l/cbCaihRQGgVbYDBS","pdf",4773609,4,1,24,"English","en",105,"# Abstract\n# Introduction\n## Challenges in Applying LLM Reasoning to Mathematics\n## The MechMath Agent Team (MMAT) and Harness Architecture","[{\"question\":\"What problem does MMAT target in mathematical research?\",\"answer\":\"MMAT addresses the difficulty of applying AI reasoning to mathematics, where derivations are non-linear, logical constraints are strict, and exploration cycles are long. It aims to overcome weaknesses of rigid, error-propagating multi-agent pipelines.\"},{\"question\":\"How does the tripartite Harness Architecture work in MMAT?\",\"answer\":\"MMAT separates responsibilities into Control, Execution, and Augmentation planes. The Control Plane schedules tasks using deterministic structures, the Execution Plane uses sandboxed workspaces and file-based handoffs, and the Augmentation Plane supports human-AI refinement and continual memory constraints.\"},{\"question\":\"How are formally certified proofs produced in MMAT?\",\"answer\":\"MMAT instantiates three specialized agents—KB-Manager, Natural Language Prover, and Formal Language Prover—in a closed loop. This workflow enables formally certified mathematical proofs rather than purely informal reasoning.\"}]",1784197902,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mechmath-agent-team-llm-driven-agents-for-mathematical-research","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/mechmath-agent-team-llm-driven-agents-for-mathematical-research/84730/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does MMAT target in mathematical research?","Question",{"text":75,"@type":76},"MMAT addresses the difficulty of applying AI reasoning to mathematics, where derivations are non-linear, logical constraints are strict, and exploration cycles are long. It aims to overcome weaknesses of rigid, error-propagating multi-agent pipelines.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the tripartite Harness Architecture work in MMAT?",{"text":80,"@type":76},"MMAT separates responsibilities into Control, Execution, and Augmentation planes. The Control Plane schedules tasks using deterministic structures, the Execution Plane uses sandboxed workspaces and file-based handoffs, and the Augmentation Plane supports human-AI refinement and continual memory constraints.",{"name":82,"@type":73,"acceptedAnswer":83},"How are formally certified proofs produced in MMAT?",{"text":84,"@type":76},"MMAT instantiates three specialized agents—KB-Manager, Natural Language Prover, and Formal Language Prover—in a closed loop. This workflow enables formally certified mathematical proofs rather than purely informal reasoning.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]