The FM Agent
Annan Li, Chufan Wu, Zengle Ge, Yee Hin Chong, Zhinan Hou, Lizhe Cao, Cheng Ju, Jianmin Wu, Huaiming Li, Haobo Zhang, Shenghao Feng, Mo Zhao, Fengzhi Qiu, Rui Yang, Mengmeng Zhang, Wenyi Zhu, Yingying Sun, Quan Sun, Shunhao Yan, Danyu Liu, Dawei Yin, Dou Shen

TL;DR
FM Agent is a versatile multi-agent system that combines LLM reasoning with evolutionary search to autonomously solve complex scientific and engineering problems, achieving state-of-the-art results across diverse domains.
Contribution
It introduces a novel framework integrating expert-guided initialization, evolutionary sampling, domain evaluators, and distributed infrastructure for autonomous problem-solving.
Findings
Achieves state-of-the-art results on multiple benchmarks.
Demonstrates 20x speedups in kernel optimization.
Successfully solves classical mathematical problems autonomously.
Abstract
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…
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