arXiv Machine Learning August 4, 2026 By Richard Zhu, Kento Nishi

Evolutionary Curriculum Learning Improves Biological Sequence Modeling

Read the original on arXiv Machine Learning →

arXiv:2608. 00697v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) trained on multiple sequence alignments (MSAs) have emerged as powerful generative models for biological sequences, with applications ranging from disease variant prediction to functional RNA design.

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

arXiv Machine Learning

Jun 26

Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

arXiv:2603. 14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples.

By Jeffrey D. Varner

llms safety

arXiv Machine Learning

Jun 18

Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment

arXiv:2606. 18703v1 Announce Type: new Abstract: Pretrained biological language models expose per-token probability distributions through masked-token prediction, providing the likelihood interface central to sequence design, variant scoring, and mechanistic interpretation.

By Yanjun Shao, Yundi Chen, Yashvi Patel, Aurelien Pelissier, Mar\'ia Rodr\'iguez Mart\'inez

llms rag nlp multimodal benchmarks safety

arXiv Machine Learning

Jun 30

Transformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS

arXiv:2606. 28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design.

By Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge, Brook Byrns, Heather L. Wilson, Sureesh Tikoo, Steven Rayan, Gordon Broderick

llms

arXiv Machine Learning

Jun 4

Towards A Generative Protein Evolution Machine with DPLM-Evo

arXiv:2605. 00182v3 Announce Type: replace Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints.

By Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng, Yu Li, Shujian Huang, Quanquan Gu

llms diffusion benchmarks safety

arXiv AI

Jul 24

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances

arXiv:2511. 03354v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is transforming bioinformatics by advancing genomics, proteomics, transcriptomics, structural biology, and drug discovery.

By Wasimul Karim, Riasad Alvi, Sayeem Been Zaman, Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Saddam Mukta, Md Rafi Ur Rashid, Md Rafiqul Islam, Yakub Sebastian, Sami Azam

benchmarks safety

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

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Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

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