@inproceedings{liu-etal-2026-researchbench,
    title = "{R}esearch{B}ench: Benchmarking {LLM}s in Scientific Discovery via Inspiration-Based Task Decomposition",
    author = "Liu, Yujie  and
      Yang, Zonglin  and
      Xie, Tong  and
      Ni, Jinjie  and
      Gao, Ben  and
      Li, Yuqiang  and
      Tang, Shixiang  and
      Ouyang, Wanli  and
      Cambria, Erik  and
      Zhou, Dongzhan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.644/",
    pages = "13187--13207",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we introduce the first large-scale benchmark for evaluating LLMs on a sufficient set of scientific discovery sub-tasks{---}inspiration retrieval, hypothesis composition, and hypothesis ranking{---}where sufficient means that perfectly solving these sub-tasks perfectly solves the overall discovery task. We develop an automated LLM-based framework that extracts critical components{---}research questions, background surveys, inspirations, and hypotheses{---}from papers across 12 disciplines, with expert validation confirming its accuracy. To prevent data contamination, we focus exclusively on publications from 2024 onward, ensuring minimal overlap with LLM pretraining data; our automated framework further enables automatic extraction of even more recent papers as LLM pretraining cutoffs advance, supporting scalable and contamination-free automatic renewal of this discovery benchmark. Our evaluation shows that, across disciplines, LLMs excel at inspiration retrieval{---}an out-of-distribution task{---}suggesting their ability to surface novel knowledge associations."
}