EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

Xinyu Zhu; Yuzhu Cai; Zexi Liu; Cheng Wang; Fengyang Li; Wenkai Jin; Wanxu Liu; Zehao Bing; Bingyang Zheng; Jingyi Chai; Shuo Tang; Rui Ye; Yuwen Du; Xianghe Pang; Yaxin Du; Tingjia Miao; Yuzhi Zhang; Ruoxue Liao; Zhaohan Ding; Linfeng Zhang; Yanfeng Wang; Weinan E; Siheng Chen

arXiv · 2604.17406

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

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Abstract

The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt across scientific fields (loop fragmentation) and useful evidence and experience are lost in long-horizon research (loop discontinuity), bringing obstacles to Agentic Science at Scale. We introduce EvoMaster, a foundational evolving agent framework for Agentic Science at Scale. EvoMaster handles loop fragmentation and loop discontinuity by implementing Loop Research in which external evidence persists and improves later decisions. Through three nested loops, Execution, Exploration and Evolution (E$^3$), loop research connects research within runs, across experiments and across studies. Across ten benchmarks spanning scientific research, coding and reasoning, EvoMaster achieves the best score among four agents using GPT-5.4, reaching a mean score of 58.02%, and outperforms the strongest competing agent Codex(40.29%) while costing 35.6% less. These results show that a shared loop-research foundation can support diverse scientific agents at scale.

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Xinyu Zhu, Yuzhu Cai, Zexi Liu, Cheng Wang, Fengyang Li, Wenkai Jin, Wanxu Liu, Zehao Bing, Bingyang Zheng, Jingyi Chai, Shuo Tang, Rui Ye, Yuwen Du, Xianghe Pang, Yaxin Du, Tingjia Miao, Yuzhi Zhang, Ruoxue Liao, Zhaohan Ding, Linfeng Zhang, Yanfeng Wang, Weinan E, Siheng Chen. 2026-04-19. EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale. https://arxiv.org/abs/2604.17406

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