EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform complex end-to-end scientific discovery tasks requiring coordination of specialized roles, including idea generation and experimental execution. However, most state-of-the-art AI scientist systems rely on static, hand-designed pipelines and fail to adapt based on accumulated interaction histories. As a result, these systems overlook promising research directions, repeat failed experiments, and pursue infeasible ideas. To address this, we introduce EvoScientist, an evolving multi-agent AI scientist framework that continuously improves research strategies through persistent memory and self-evolution. EvoScientist comprises three specialized agents: a Researcher Agent (RA) for scientific idea generation, an Engineer Agent (EA) for experiment implementation and execution, and an Evolution Manager Agent (EMA) that distills insights from prior interactions into reusable knowledge. EvoScientist contains two persistent memory modules: (i) an ideation memory, which summarizes feasible research directions from top-ranked ideas while recording previously unsuccessful directions; and (ii) an experimentation memory, which captures effective data processing and model training strategies derived from code search trajectories and best-performing implementations. These modules enable the RA and EA to retrieve relevant prior strategies, improving idea quality and code execution success rates over time. Experiments show that EvoScientist outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, achieving higher novelty, feasibility, relevance, and clarity via automatic and human evaluation. EvoScientist also substantially improves code execution success rates through multi-agent evolution, demonstrating persistent memory's effectiveness for end-to-end scientific discovery.
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Yougang Lyu
claimed_verified
2026-03-18T08:46:23.927Z
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youganglyu
youganglyu
user
youganglyu
69b3c9882ad6c5d4cf486c73
Xi Zhang
claimed_verified
2026-03-23T10:59:25.351Z
652ebdcc76365388909b06cf
https://cdn-avatars.huggingface.co/v1/production/uploads/652ebdcc76365388909b06cf/MnIVVuTphX8buEER9EkCY.jpeg
Xi Zhang
X-iZhang
user
X-iZhang
69b3c9882ad6c5d4cf486c74
Xinhao Yi
69b3c9882ad6c5d4cf486c75
Yuyue Zhao
69b3c9882ad6c5d4cf486c76
Shuyu Guo
69b3c9882ad6c5d4cf486c77
Wenxiang Hu
69b3c9882ad6c5d4cf486c78
Jan Piotrowski
claimed_verified
2026-03-17T09:55:48.908Z
683b1c85e1526aee26fdb4b4
/avatars/74084d15af24d8ef9d89b43f42b2a649.svg
Jan Piotrowski
SemiGlassFace
user
SemiGlassFace
69b3c9882ad6c5d4cf486c79
Jakub Kaliski
69b3c9882ad6c5d4cf486c7a
Jacopo Urbani
69b3c9882ad6c5d4cf486c7b
Zaiqiao Meng
69b3c9882ad6c5d4cf486c7c
Lun Zhou
69b3c9882ad6c5d4cf486c7d
Xiaohui Yan
EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform complex end-to-end scientific discovery tasks requiring coordination of specialized roles, including idea generation and experimental execution. However, most state-of-the-art AI scientist systems rely on static, hand-designed pipelines and fail to adapt based on accumulated interaction histories. As a result, these systems overlook promising research directions, repeat failed experiments, and pursue infeasible ideas. To address this, we introduce EvoScientist, an evolving multi-agent AI scientist framework that continuously improves research strategies through persistent memory and self-evolution. EvoScientist comprises three specialized agents: a Researcher Agent (RA) for scientific idea generation, an Engineer Agent (EA) for experiment implementation and execution, and an Evolution Manager Agent (EMA) that distills insights from prior interactions into reusable knowledge. EvoScientist contains two persistent memory modules: (i) an ideation memory, which summarizes feasible research directions from top-ranked ideas while recording previously unsuccessful directions; and (ii) an experimentation memory, which captures effective data processing and model training strategies derived from code search trajectories and best-performing implementations. These modules enable the RA and EA to retrieve relevant prior strategies, improving idea quality and code execution success rates over time. Experiments show that EvoScientist outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, achieving higher novelty, feasibility, relevance, and clarity via automatic and human evaluation. EvoScientist also substantially improves code execution success rates through multi-agent evolution, demonstrating persistent memory's effectiveness for end-to-end scientific discovery.
EvoScientist is an adaptive multi-agent framework that enhances scientific discovery by continuously learning from past interactions through persistent memory modules.
Large Language Models multi-agent AI scientist persistent memory ideation memory experimentation memory Researcher Agent Engineer Agent Evolution Manager Agent scientific idea generation experiment implementation code execution success rates