@inproceedings{gu-etal-2026-mori,
    title = "{M}o{RI}: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models",
    author = "Gu, Chenyang  and
      Cheng, Jiahao  and
      Zhang, Meicong  and
      Zheng, Pujun  and
      Zheng, Jinquan  and
      He, Guoxiu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1609/",
    pages = "34838--34869",
    ISBN = "979-8-89176-390-6",
    abstract = "Scientific ideation aims to propose novel solutions within a given scientific context. Existing LLM-based agentic approaches emulate human research workflows, yet inadequately model scientific reasoning, resulting in surface-level conceptual recombinations that lack technical depth and scientific grounding. To address this issue, we propose $\textbf{MoRI}$ ($\textbf{Mo}$tivation-grounded $\textbf{R}$easoning for Scientific $\textbf{I}$deation), a framework that enables LLMs to explicitly learn the reasoning process from research motivations to methodologies. The base LLM is initialized via supervised fine-tuning to generate a research motivation from a given context, and is subsequently trained under a composite reinforcement learning reward that approximates scientific rigor: (1) entropy-aware information gain encourages the model to uncover and elaborate high-complexity technical details grounded in ground-truth methodologies, and (2) contrastive semantic gain constrains the reasoning trajectory to remain conceptually aligned with scientifically valid solutions. Empirical results show that MoRI consistently outperforms strong commercial LLMs and complex agentic baselines across multiple dimensions, including novelty, technical rigor, and feasibility. The code is available on GitHub."
}