{"ID":2886688,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02066","arxiv_id":"2508.02066","title":"MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs","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 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.","short_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 interpretab...","url_abs":"https://arxiv.org/abs/2508.02066","url_pdf":"https://arxiv.org/pdf/2508.02066v2","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\"]","published":"2025-08-04T05:10:11Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}