arXiv Machine Learning June 26, 2026 By Jeffrey D. Varner

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

Read the original on arXiv Machine Learning →

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.

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

arXiv Machine Learning

Aug 10

Conditioning Protein Generation via Hopfield Pattern Multiplicity

arXiv:2603. 20115v2 Announce Type: replace Abstract: Small protein-family alignments often contain a subset of interest but not enough labeled data to train a conditional generator.

By Jeffrey D. Varner

diffusion

arXiv Machine Learning

Aug 4

Evolutionary Curriculum Learning Improves Biological Sequence Modeling

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.

By Richard Zhu, Kento Nishi

benchmarks safety

arXiv AI

Jun 9

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

arXiv:2507. 08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.

By Changze Lv, Jiang Zhou, Siyu Long, Lihao Wang, Jiangtao Feng, Dongyu Xue, Yu Pei, Hao Wang, Zherui Zhang, Yuchen Cai, Zhiqiang Gao, Ziyuan Ma, Jiakai Hu, Chaochen Gao, Jingjing Gong, Yuxuan Song, Shuyi Zhang, Xiaoqing Zheng, Deyi Xiong, Lei Bai, Wanli Ouyang, Ya-Qin Zhang, Wei-Ying Ma, Bowen Zhou, Hao Zhou

llms safety

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

Aug 12

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

arXiv:2602. 17162v3 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature".

By Ariel Larey, Elay Dahan, Amit Bleiweiss, Raizy Kellerman, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini, Gideon Rechavi, Nicole Bussola, Simon Lee, Shane O'Connell, Dung Hoang, Marissa Wirth, Alexander W. Charney, Nati Daniel, Yoli Shavit

rag diffusion benchmarks safety

arXiv AI

Jun 26

Autoregressive Boltzmann Generators

arXiv:2606. 27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics.

By Danyal Rehman, Charlie B. Tan, Yoshua Bengio, Avishek Joey Bose, Alexander Tong

llms benchmarks

Related stories

Conditioning Protein Generation via Hopfield Pattern Multiplicity

Evolutionary Curriculum Learning Improves Biological Sequence Modeling

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

Towards A Generative Protein Evolution Machine with DPLM-Evo

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

Autoregressive Boltzmann Generators