Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
- URL: http://arxiv.org/abs/2506.18348v3
- Date: Fri, 01 Aug 2025 10:35:50 GMT
- Title: Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
- Authors: Weilun Yu, Shixiang Tang, Yonggui Huang, Nanqing Dong, Li Fan, Honggang Qi, Wei Liu, Xiaoli Diao, Xi Chen, Wanli Ouyang,
- Abstract summary: IDVSCI is a multi-agent framework built on large language models (LLMs)<n>It incorporates two key innovations: a Dynamic Knowledge Exchange mechanism and a Dual-Diversity Review paradigm.<n>Results show that IDVSCI consistently achieves the best performance across two datasets.
- Score: 53.38438460574943
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 scientist agents show promise in autonomous scientific discovery, they often lack the interactive reasoning and evaluation mechanisms essential to real-world research. We propose IDVSCI (Internal Discussion and Vote SCIentists), a multi-agent framework built on LLMs that incorporates two key innovations: a Dynamic Knowledge Exchange mechanism enabling iterative feedback among agents, and a Dual-Diversity Review paradigm that simulates heterogeneous expert evaluation. These components jointly promote deeper reasoning and the generation of more creative and impactful scientific ideas. To evaluate the effectiveness and generalizability of our approach, we conduct experiments on two datasets: a widely used benchmark in computer science and a new dataset we introduce in the health sciences domain. Results show that IDVSCI consistently achieves the best performance across both datasets, outperforming existing systems such as AI Scientist and VIRSCI. These findings highlight the value of modeling interaction and peer review dynamics in LLM-based autonomous research.
Related papers
- The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist [3.7803247326675162]
Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs)<n>This survey proposes a comprehensive framework to categorize the evolving roles of LLMs in scientific innovation across three hierarchical levels: Evaluator, Collaborator, and Scientist.
arXiv Detail & Related papers (2025-07-16T00:11:01Z) - ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
arXiv Detail & Related papers (2025-05-26T12:27:27Z) - Towards Artificial Intelligence Research Assistant for Expert-Involved Learning [64.7438151207189]
Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research.<n>We present textbfARtificial textbfIntelligence research assistant for textbfExpert-involved textbfLearning (ARIEL)
arXiv Detail & Related papers (2025-05-03T14:21:48Z) - IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery [27.218896203253987]
IRIS is an open-source platform designed for researchers to leverage large language models (LLMs)-assisted scientific ideation.<n>IRIS incorporates innovative features to enhance ideation, including adaptive test-time compute expansion via Monte Carlo Tree Search (MCTS), fine-grained feedback mechanism, and query-based literature synthesis.<n>We conduct a user study with researchers across diverse disciplines, validating the effectiveness of our system in enhancing ideation.
arXiv Detail & Related papers (2025-04-23T14:01:36Z) - Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents [11.74019905854637]
Large language models (LLMs) are evolving into scientific agents that automate critical tasks.<n>Unlike general-purpose LLMs, specialized agents integrate domain-specific knowledge, advanced tool sets, and robust validation mechanisms.<n>We highlight why they differ from general agents and the ways in which they advance research across various scientific fields.
arXiv Detail & Related papers (2025-03-31T13:11:28Z) - Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research.<n>VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas.<n>We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
arXiv Detail & Related papers (2024-10-12T07:16:22Z) - LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.