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 for interactive hypothesis generationm, 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 querybased 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 here.