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arXiv · 2605.24018

EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery

Xiaoyu Xiong·Yuqi Ren·Deyi Xiong
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Abstract

Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design of research workflows and multi-role collaboration mechanisms. To mitigate these issues, we propose EvoSci, a multi-agent scientific collaboration framework, which integrates bio-inspired evolution with knowledge graph modeling. To iteratively generate, evaluate, and refine research ideas, EvoSci incorporates multiple role-based agents, including mentor, researcher, and reviewer. By combining collaborative reasoning, shared memory, and evolutionary feedback, EvoSci significantly enhances the coherence and creativity of scientific exploration. Experiments on real-world research topics demonstrate that EvoSci significantly outperforms strong baselines in LLM-based structured peer-review and comparative ranking evaluations, achieving the highest overall peer-review score (ICLR 4.90) and top ranking (Top-10 = 54). These results suggest its superiority in both scientific idea generation and continuous discovery.

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Xiaoyu Xiong, Yuqi Ren, Deyi Xiong. 2026-05-20. EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery. https://arxiv.org/abs/2605.24018

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