5 papers · 1 filter
Self-evolving AI agents for protein discovery and directed evolution
Yang Tan, Lingrong Zhang, Mingchen Li +5
Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFacto…
Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
Weilun Yu, Shixiang Tang, Yonggui Huang +7
Scientific progress increasingly relies on effective collaboration among researchers, a dynamic that large language models (LLMs) have only begun to emulate. While recent LLM-based…
Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System
Haoyang Su, Renqi Chen, Shixiang Tang +10
The rapid advancement of scientific progress requires innovative tools that can accelerate knowledge discovery. Although recent AI methods, particularly large language models (LLMs…
AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research
Renqi Chen, Haoyang Su, Shixiang Tang +7
The Science of Science (SoS) explores the mechanisms underlying scientific discovery, and offers valuable insights for enhancing scientific efficiency and fostering innovation. Tra…
Toward Understanding BERT-Like Pre-Training for DNA Foundation Models
Chaoqi Liang, Lifeng Qiao, Peng Ye +9
With the success of large-scale pre-training in language tasks, there is an increasing trend of applying it to the domain of life sciences. In particular, pre-training methods base…