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.
@article{arxiv.2508.02066,
title = {MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs},
author = {Guojiang Zhao and Zixiang Lu and Yutang Ge and Sihang Li and Zheng Cheng and Haitao Lin and Lirong Wu and Hanchen Xia and Hengxing Cai and Wentao Guo and Hongshuai Wang and Mingjun Xu and Siyu Zhu and Guolin Ke and Linfeng Zhang and Zhifeng Gao},
journal= {arXiv preprint arXiv:2508.02066},
year = {2026}
}