用仿生进化机制提升大模型科研协作能力
EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery
- 设计多角色代理模拟科研流程，通过协同推理与反馈迭代优化
- 在真实课题上达成ICLR评分4.90，排名前10占比54%
- 适合需要持续创新的科研团队和自动化研究探索场景
大型语言模型在科学发现中展现出巨大潜力，但现有方法在研究流程设计和多角色协作方面仍面临挑战。为此，我们提出EvoSci，一个融合生物启发式进化与知识图谱建模的多智能体科研协作框架。该框架通过包含导师、研究员和评审员在内的多角色代理，结合协同推理、共享记忆与进化反馈，实现研究思路的迭代生成、评估与优化。实验表明，EvoSci在基于LLM的结构化同行评审与对比排名评估中显著优于强基线，在ICLR评审中获得4.90的最高分，前10名占比达54%。结果证明其在科学创意生成与持续发现方面的优势。
原文摘要 · Abstract (English)
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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