Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Shanghai Institute for Science of Science（上海科学研究院）；Department of Information Engineering, Chinese University of Hong Kong（香港中文大学信息工程系）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
The rapid advancement of scientific progress requires innovative tools that can accelerate knowledge discovery. Although recent AI methods, particularly large language models (LLMs), have shown promise in tasks such as hypothesis generation and experimental design, they fall short of replicating the collaborative nature of real-world scientific practices, where diverse experts work together in teams to tackle complex problems. To address the limitations, we propose an LLM-based multi-agent system, i.e., Virtual Scientists (VirSci), designed to mimic the teamwork inherent in scientific research. VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas. Through comprehensive experiments, we demonstrate that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas. We further investigate the collaboration mechanisms that contribute to its tendency to produce ideas with higher novelty, offering valuable insights to guide future research and illuminating pathways toward building a robust system for autonomous scientific discovery. The code is available at https://github.com/open-sciencelab/Virtual-Scientists.
Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Bank of China（中国银行）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）；State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所非线性力学重点实验室）
FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration
University of Chinese Academy of Sciences（中国科学院大学）；Dalian University of Technology（大连理工大学）；UNSW Sydney（新南威尔士大学悉尼分校）；Shenzhen University of Advanced Technology（深圳理工大学）；Xiamen University（厦门大学）
Can Large Language Models Unlock Novel Scientific Research Ideas?
Department of Computer Science and Engineering, Indian Institute of Technology Patna（计算机科学与工程系，印度理工学院帕纳布分校）；National Center for Computational Sciences, Oak Ridge National Laboratory（计算科学国家中心，橡树岭国家实验室）
Harnessing Large Language Models for Scientific Novelty Detection
Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
CoAuthorAI: A Human in the Loop System For Scientific Book Writing
Kexin Technology, Beijing（凯欣科技，北京）；Institute for Sustainable Industries and Liveable Cities, Victoria University（可持续产业与宜居城市研究所，维多利亚大学）；School of Information Technology, Deakin University（信息科技学院，德坎大学）
SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Science and Technology Beijing（北京科技大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
SciMON: Scientific Inspiration Machines Optimized for Novelty
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Allen Institute for Artificial Intelligence (AI2)（艾伦人工智能研究所）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
PaperRobot: Incremental Draft Generation of Scientific Ideas
Rensselaer Polytechnic Institute（伦斯勒理工学院）；DiDi Labs（滴滴实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Washington（华盛顿大学）
Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
School of Computing Newcastle University（计算学院新castle大学）
LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research
Boston University（波士顿大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Delaware（特拉华大学）；Acaciawood Preparatory Academy（阿卡西亚伍德预备学院）
Agentic AI for Gravitational Wave Data Analysis: A Head-to-Head Comparison of Coding Agents Executing a Matched Filter Pipeline on Einstein Telescope Simulated Data
Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
Michigan State University（密歇根州立大学）；CREATE for STEM（STEM创新计划）；University of Houston（休斯顿大学）
