MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models

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 maintain conceptually aligned with scientifically valid solutions. Empirical results show that MoRI significantly outperforms strong commercial LLMs and complex agentic baselines across multiple dimensions, including novelty, technical rigor, and feasibility. The code will be made available on \href{https://github.com/ECNU-Text-Computing/IdeaGeneration}{GitHub}.

Figures (13)

• Figure 1: Conceptual comparison. Unlike existing approaches that rely on pattern recombination or computationally expensive external scaffolding, MoRI internalizes scientific ideation through learning motivation-grounded reasoning. It initially identifies a Motivation ($m$) from a given Context ($x$), then generates a Reasoning Trajectory ($z$) to deduce a grounded Methodology ($y$), which is optimized via our composite RL rewards.
• Figure 2: Overview of MoRI. Our framework optimizes reasoning via GRPO (Bottom) using composite rewards: Entropy-Aware Information Gain (Left) for high-entropy explanation and technical depth and Contrastive Semantic Gain (Right) for logical direction alignment, modulated by Length Anchoring (Center) to enforce reasoning depth.
• Figure 3: Training Dynamics. Moving average of CoT Length (a), Shaped EAIG (b), and Shaped Semantic Score (c).
• Figure 4: Entropy Mask Dynamics. Moving average of CoT Length (a), Shaped EAIG (b), and Shaped Semantic Score (c).
• Figure 5: Impact of Length Anchoring. Moving average of CoT Length (a), Shaped EAIG (b), and Shaped Semantic Score (c).