AI RESEARCH
MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models
arXiv CS.CL
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ArXi:2603.19044v1 Announce Type: new 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.