arXiv AI June 11, 2026 By Xinni Zhang, Zijing Liu, He Cao, Yu Li, Irwin King

Augmenting Molecular Language Models with Local $n$-gram Memory

arXiv:2606. 12113v1 Announce Type: cross Abstract: Transformer-based language models for SMILES strings suffer from a locality gap: standard character-level tokenization fragments chemically meaningful motifs, forcing models to repeatedly learn local syntax at the expense of long-range dependencies.

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

arXiv AI

Jun 3

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.

By Arun Raja, Garrett M. Morris, Kian Ming A. Chai

llms nlp benchmarks

arXiv Machine Learning

Jun 9

VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning

arXiv:2605. 16823v2 Announce Type: replace Abstract: Large language models succeed by combining large-scale pretraining with meaningful discrete tokens.

By Takayuki Kimura

llms nlp efficiency

arXiv Machine Learning

Jul 3

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

arXiv:2607. 02140v1 Announce Type: new Abstract: Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substructures they encode.

By Anna Karnysheva, Dietrich Klakow, Ji-Ung Lee

llms fine-tuning

arXiv Machine Learning

Aug 4

SIGMA: Semantic Identifier Grouping for Molecular Autoregression

arXiv:2603. 25062v2 Announce Type: replace Abstract: Autoregressive molecular models assign probability to molecular serializations even though chemical identity is invariant to serialization.

By Xinyu Wang, Fei Dou, Jinbo Bi, Minghu Song

benchmarks safety

arXiv Machine Learning

Jun 5

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han

llms rag fine-tuning

arXiv AI

Jul 16

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools

arXiv:2607. 13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representations under-specify key graph-topological cues.

By Konstantinos Bougiatiotis, Dimitrios Kelesis, Georgios Paliouras

llms agents

Related stories

Rethinking Molecular Text Representations for LLMs: An Empirical Study

VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning

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

SIGMA: Semantic Identifier Grouping for Molecular Autoregression

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools