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

摘要

The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform increasingly complex end-to-end scientific discovery tasks. Such tasks required the coordination of specialized roles, including idea generation and experimental execution. Despite this complexity, most state-of-the-art AI scientist systems rely on static, hand-designed pipelines and fail to adapt their idea- or code-generation strategies based on accumulated interaction histories. As a result, these systems systematically overlook promising research directions, repeat previously failed experiments, and pursue infeasible ideas. To address this limitation, we introduce EvoScientist, an evolving multi-agent AI scientist framework that continuously improves its research strategies through persistent memory and self-evolution. EvoScientist comprises three specialized agents: a Researcher Agent (RA) responsible for scientific idea generation, an Engineer Agent (EA) responsible for experiment implementation and execution, and an Evolution Manager Agent (EMA) that distills insights from prior agent interactions into reusable knowledge. Specifically, EvoScientist contains two persistent memory modules: (i) an ideation memory, which summarizes feasible research directions from top-ranked ideas while recording previously unsuccessful directions identified during idea validation; and (ii) an experimentation memory, which captures effective data processing and model training strategies derived from code search trajectories and best-performing implementations. These memory modules enable the RA and EA to retrieve relevant prior strategies, thereby improving idea quality and increasing 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 performance in terms of novelty, feasibility, relevance, and clarity through automatic and human evaluation. Furthermore, EvoScientist substantially improves code execution success rates through multi-agent evolution, demonstrating the effectiveness of persistent memory for end-to-end scientific discovery.