IRIS让研究人员用AI生成科学假说，支持交互式调整和文献整合。
IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery
- 通过蒙特卡洛树搜索动态扩展推理能力，提升假说质量
- 支持细粒度反馈与查询式文献整合，增强研究可控性
- 适合跨学科科研人员快速探索新思路，开源可复现
大语言模型（LLM）的快速发展提出了一个关键问题：如何加速科学发现？本文聚焦研究的第一阶段——生成新颖假说。现有自动化假说生成方法多依赖多智能体框架或扩展测试时计算，但缺乏透明性和可引导性。为此，我们提出IRIS：交互式科研构想系统，一个开源平台，帮助研究人员利用LLM进行科学构想。IRIS引入创新功能，包括基于蒙特卡洛树搜索（MCTS）的自适应测试时计算扩展、细粒度反馈机制及基于查询的文献合成。该系统旨在赋予研究者在构想过程中更大的控制力与洞察力。我们还开展了跨学科用户研究，验证了系统在提升构想效率方面的有效性。代码已开源至https://github.com/Anikethh/IRIS-Interactive-Research-Ideation-System。
原文摘要 · Abstract (English)
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 at https://github.com/Anikethh/IRIS-Interactive-Research-Ideation-System
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