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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.26144 |
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| _version_ | 1866911474276892672 |
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| author | Li, Annan Wu, Chufan Ge, Zengle Chong, Yee Hin Hou, Zhinan Cao, Lizhe Ju, Cheng Wu, Jianmin Li, Huaiming Zhang, Haobo Feng, Shenghao Zhao, Mo Qiu, Fengzhi Yang, Rui Zhang, Mengmeng Zhu, Wenyi Sun, Yingying Sun, Quan Yan, Shunhao Liu, Danyu Yin, Dawei Shen, Dou |
| author_facet | Li, Annan Wu, Chufan Ge, Zengle Chong, Yee Hin Hou, Zhinan Cao, Lizhe Ju, Cheng Wu, Jianmin Li, Huaiming Zhang, Haobo Feng, Shenghao Zhao, Mo Qiu, Fengzhi Yang, Rui Zhang, Mengmeng Zhu, Wenyi Sun, Yingying Sun, Quan Yan, Shunhao Liu, Danyu Yin, Dawei Shen, Dou |
| contents | Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purpose multi-agent framework that leverages a synergistic combination of LLM-based reasoning and large-scale evolutionary search to address complex real-world challenges. The core of FM Agent integrates several key innovations: 1) a cold-start initialization phase incorporating expert guidance, 2) a novel evolutionary sampling strategy for iterative optimization, 3) domain-specific evaluators that combine correctness, effectiveness, and LLM-supervised feedback, and 4) a distributed, asynchronous execution infrastructure built on Ray. Demonstrating broad applicability, our system has been evaluated across diverse domains, including operations research, machine learning, GPU kernel optimization, and classical mathematical problems. FM Agent reaches state-of-the-art results autonomously, without human interpretation or tuning -- 1976.3 on ALE-Bench (+5.2\%), 43.56\% on MLE-Bench (+4.0pp), up to 20x speedups on KernelBench, and establishes new state-of-the-art(SOTA) results on several classical mathematical problems. Beyond academic benchmarks, FM Agent shows considerable promise for both large-scale enterprise R\&D workflows and fundamental scientific research, where it can accelerate innovation, automate complex discovery processes, and deliver substantial engineering and scientific advances with broader societal impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The FM Agent Li, Annan Wu, Chufan Ge, Zengle Chong, Yee Hin Hou, Zhinan Cao, Lizhe Ju, Cheng Wu, Jianmin Li, Huaiming Zhang, Haobo Feng, Shenghao Zhao, Mo Qiu, Fengzhi Yang, Rui Zhang, Mengmeng Zhu, Wenyi Sun, Yingying Sun, Quan Yan, Shunhao Liu, Danyu Yin, Dawei Shen, Dou Artificial Intelligence Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purpose multi-agent framework that leverages a synergistic combination of LLM-based reasoning and large-scale evolutionary search to address complex real-world challenges. The core of FM Agent integrates several key innovations: 1) a cold-start initialization phase incorporating expert guidance, 2) a novel evolutionary sampling strategy for iterative optimization, 3) domain-specific evaluators that combine correctness, effectiveness, and LLM-supervised feedback, and 4) a distributed, asynchronous execution infrastructure built on Ray. Demonstrating broad applicability, our system has been evaluated across diverse domains, including operations research, machine learning, GPU kernel optimization, and classical mathematical problems. FM Agent reaches state-of-the-art results autonomously, without human interpretation or tuning -- 1976.3 on ALE-Bench (+5.2\%), 43.56\% on MLE-Bench (+4.0pp), up to 20x speedups on KernelBench, and establishes new state-of-the-art(SOTA) results on several classical mathematical problems. Beyond academic benchmarks, FM Agent shows considerable promise for both large-scale enterprise R\&D workflows and fundamental scientific research, where it can accelerate innovation, automate complex discovery processes, and deliver substantial engineering and scientific advances with broader societal impact. |
| title | The FM Agent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26144 |