EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery
EvoSci is a proposed multi-agent framework designed to enhance scientific discovery through bio-inspired evolution and knowledge graph modeling. It incorporates various role-based agents to improve collaboration and idea generation in research workflows. Experimental results indicate that EvoSci outperforms existing methods in peer-review evaluations and idea generation.
- ▪EvoSci integrates bio-inspired evolution with knowledge graph modeling to improve scientific collaboration.
- ▪The framework includes multiple role-based agents such as mentor, researcher, and reviewer.
- ▪EvoSci achieved the highest overall peer-review score and top ranking in evaluations, demonstrating its effectiveness.
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Computer Science > Artificial Intelligence arXiv:2605.24018 (cs) [Submitted on 20 May 2026] Title:EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery Authors:Xiaoyu Xiong, Yuqi Ren, Deyi Xiong View a PDF of the paper titled EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery, by Xiaoyu Xiong and 2 other authors View PDF HTML (experimental) 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.
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