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.

翻译：大语言模型在科学发现中展现出强大潜力，但现有方法在研究工作流设计与多角色协作机制方面仍面临重大挑战。针对这些问题，我们提出EvoSci——一种融合生物启发式演化与知识图谱建模的多智能体科学协作框架。该框架通过整合导师、研究员和评审者等多角色智能体，实现研究思路的迭代生成、评估与优化。结合协作推理、共享记忆与演化反馈机制，EvoSci显著提升了科学探索的连贯性与创新性。在真实研究主题上的实验表明，EvoSci在基于大语言模型的结构化同行评议及比较排名评估中显著优于强基线方法，取得了最高同行评议总分（ICLR 4.90）及最优排名（Top-10指标得分54）。这些结果证明了该方法在科学思想生成与持续发现方面的卓越性能。