ResearchAgent: Iterative Research Idea Generation over Scientific
  Literature with Large Language Models
Jinheon Baek Sujay Kumar Jauhar Silviu Cucerzan Sung Ju Hwang
Scientific Research, vital for improving human life, is hindered by its
inherent complexity, slow pace, and the need for specialized experts. To
enhance its productivity, we propose a ResearchAgent, a large language
model-powered research idea writing agent, which automatically generates
problems, methods, and experiment designs while iteratively refining them based
on scientific literature. Specifically, starting with a core paper as the
primary focus to generate ideas, our ResearchAgent is augmented not only with
relevant publications through connecting information over an academic graph but
also entities retrieved from an entity-centric knowledge store based on their
underlying concepts, mined and shared across numerous papers. In addition,
mirroring the human approach to iteratively improving ideas with peer
discussions, we leverage multiple ReviewingAgents that provide reviews and
feedback iteratively. Further, they are instantiated with human
preference-aligned large language models whose criteria for evaluation are
derived from actual human judgments. We experimentally validate our
ResearchAgent on scientific publications across multiple disciplines,
showcasing its effectiveness in generating novel, clear, and valid research
ideas based on human and model-based evaluation results.