arXiv Machine Learning July 3, 2026 By Jiatong Li, Weida Wang, Changmeng Zheng, Shufei Zhang, Yatao Bian, Xiao-yong Wei, Qing Li

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis

Read the original on arXiv Machine Learning →

arXiv:2607. 01800v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete sequential tokens and the rigid topological constraints of chemical space.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Probing Chemical Language Models: Effects of Pre-training and Fine-tuning

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