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[2508.02066] MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
Summary
The paper presents MolReasoner, a two-stage framework designed to enhance molecular reasoning in large language models (LLMs), addressing issues of interpretability and hallucination in chemical tasks.
Why It Matters
As LLMs become integral in various domains, improving their reasoning capabilities in specialized fields like chemistry is crucial. MolReasoner aims to bridge the gap between general-purpose models and domain-specific applications, potentially transforming how molecular data is processed and understood.
Key Takeaways
- MolReasoner introduces a two-stage framework for molecular reasoning.
- The framework enhances interpretability and reduces hallucinations in LLM outputs.
- Extensive evaluations show significant performance improvements over existing methods.
Computer Science > Machine Learning arXiv:2508.02066 (cs) [Submitted on 4 Aug 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs Authors:Guojiang Zhao, Zixiang Lu, Yutang Ge, Sihang Li, Zheng Cheng, Haitao Lin, Lirong Wu, Hanchen Xia, Hengxing Cai, Wentao Guo, Hongshuai Wang, Mingjun Xu, Siyu Zhu, Guolin Ke, Linfeng Zhang, Zhifeng Gao View a PDF of the paper titled MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs, by Guojiang Zhao and 15 other authors View PDF HTML (experimental) Abstract: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 molec...
