提升分子大模型推理能力，让化学分析更准确可解释。
MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
- 两阶段训练：先用增强思维链数据微调，再用任务自适应奖励优化。
- 在分子生成与描述任务中显著优于多个基线模型。
- 设计更可解释，减少化学结构与文本幻觉问题，适合药物研发者使用。
大语言模型在多个领域表现优异，但在分子推理方面仍不充分。现有方法多依赖通用提示，缺乏领域特异性化学语义；或通过微调，但存在可解释性差、推理深度不足，常引发结构与文本幻觉。为此，我们提出 MolReasoner，一种两阶段框架，使模型从记忆转向高保真化学推理。第一阶段（Mol-SFT）利用知识增强的思维链（CoT）数据建立坚实基础；第二阶段（Mol-RL）通过新颖的任务自适应奖励机制优化推理，缓解幻觉。大量评估显示，MolReasoner 在分子生成和描述任务中显著超越多种强基线。进一步分析表明其设计协同性强，输出更具可解释性。本工作为实现高保真分子推理提供了系统且有效的新路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown impressive performance across various domains, but their ability to perform molecular reasoning remains underexplored. Existing methods mostly rely on general-purpose prompting, which lacks domain-specific molecular semantics, or fine-tuning, which faces challenges in interpretability and reasoning depth, often leading to structural and textual hallucinations. To address these issues, we introduce MolReasoner, a two-stage framework that transitions LLMs from memorization to high-fidelity chemical reasoning. In the Mol-SFT stage, knowledge-enhanced Chain-of-Thought (CoT) data provides a strong foundation, while the Mol-RL stage refines reasoning using a novel, task-adaptive reward system to mitigate hallucinations. Extensive evaluations demonstrate that MolReasoner significantly outperforms a wide range of strong baselines in both molecule generation and captioning tasks. Further analyses highlight the framework's synergistic design and its ability to produce more interpretable outputs. Our work presents a principled and effective new approach for advancing high-fidelity molecular reasoning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。