Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System

• URL: http://arxiv.org/abs/2410.09403v2
• Date: Wed, 19 Feb 2025 06:07:47 GMT
• Title: Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System
• Authors: Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, Nanqing Dong,
• Abstract summary: Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research. VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas. We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
• Score: 62.832818186789545
• License:
• Abstract: 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.

Related papers

• AIGS: Generating Science from AI-Powered Automated Falsification [17.50867181053229]
  We propose Baby-AIGS as a baby-step demonstration of a full-process AIGS system, which is a multi-agent system with agents in roles representing key research process. Experiments on three tasks preliminarily show that Baby-AIGS could produce meaningful scientific discoveries, though not on par with experienced human researchers.
  arXiv  Detail & Related papers  (2024-11-17T13:40:35Z)
• Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents [64.64280477958283]
  An exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions. Recent developments in large language models(LLMs) suggest a promising avenue for automating the generation of novel research ideas. We propose a Chain-of-Ideas(CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain.
  arXiv  Detail & Related papers  (2024-10-17T03:26:37Z)
• Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination [23.48126633604684]
  We contribute Scideator, a novel mixed-initiative tool for scientific ideation. Starting from a user-provided set of papers, Scideator extracts key facets (purposes, mechanisms, and evaluations) from these and relevant papers. Scideator also helps users to gauge idea novelty by searching the literature for potential overlaps.
  arXiv  Detail & Related papers  (2024-09-23T00:09:34Z)
• Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers [90.26363107905344]
  Large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery. No evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas.
  arXiv  Detail & Related papers  (2024-09-06T08:25:03Z)
• DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
  We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations. We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
  arXiv  Detail & Related papers  (2024-06-10T20:08:44Z)
• 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. ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• SciOps: Achieving Productivity and Reliability in Data-Intensive Research [0.8414742293641504]
  Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, inspiration and automation. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations.
  arXiv  Detail & Related papers  (2023-12-29T21:37:22Z)

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.