EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

Publications

Mar 9, 2026·

Yougang Lyu

Xi Zhang

Xinhao Yi

Yuyue Zhao

Shuyu Guo

Wenxiang Hu

Jan Piotrowski

Jakub Kaliski

Jacopo Urbani

Zaiqiao Meng

Lun Zhou

Xiaohui Yan

· 2 min read

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Abstract

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.

Type

Preprint

Part of the EvoScientist project — harnessing vibe research with self-evolving AI scientists.

Extend it with EvoSkills — installable skill & knowledge packs that add domain-specific expertise to AI scientists.

Overview

EvoScientist is an evolving multi-agent AI scientist framework that continuously improves its research strategies through persistent memory and self-evolution. It addresses a key limitation of existing AI-scientist systems: static, hand-designed pipelines that overlook promising directions, repeat failed experiments, and pursue infeasible ideas.

Framework

EvoScientist coordinates three specialized agents:

Researcher Agent (RA): scientific idea generation.
Engineer Agent (EA): experiment implementation and execution.
Evolution Manager Agent (EMA): distills insights from prior interactions into reusable knowledge.

These are backed by two persistent memory modules — an ideation memory (feasible directions distilled from top-ranked ideas, plus previously unsuccessful ones) and an experimentation memory (effective data-processing and training strategies from code-search trajectories and best-performing implementations) — which the RA and EA retrieve to improve idea quality and code execution success over time.

Key Results

Outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, with higher novelty, feasibility, relevance, and clarity under both automatic and human evaluation.
Substantially improves code execution success rates through multi-agent evolution, demonstrating the effectiveness of persistent memory for end-to-end scientific discovery.

BibTeX

@article{lyu2026evoscientist,
  title={EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery},
  author={Lyu, Yougang and Zhang, Xi and Yi, Xinhao and Zhao, Yuyue and Guo, Shuyu and Hu, Wenxiang and Piotrowski, Jan and Kaliski, Jakub and Urbani, Jacopo and Meng, Zaiqiao and others},
  journal={arXiv preprint arXiv:2603.08127},
  year={2026}
}