IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery

The rapid advancement in capabilities of large language models (LLMs) raises a pivotal question: How can LLMs accelerate scientific discovery? This work tackles the crucial first stage of research, generating novel hypotheses. While recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, none of the approaches effectively incorporate transparency and steerability through a synergistic Human-in-the-loop (HITL) approach. To address this gap, we introduce IRIS: Interactive Research Ideation System, an open-source platform designed for researchers to leverage LLM-assisted scientific ideation. 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. Designed to empower researchers with greater control and insight throughout the ideation process. We additionally conduct a user study with researchers across diverse disciplines, validating the effectiveness of our system in enhancing ideation. We open-source our code atthis https URL

@article{garikaparthi2025_2504.16728,
  title={ IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery },
  author={ Aniketh Garikaparthi and Manasi Patwardhan and Lovekesh Vig and Arman Cohan },
  journal={arXiv preprint arXiv:2504.16728},
  year={ 2025 }
}