arXiv Machine Learning

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.

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

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arXiv AI

Jun 4

Retrieval and competition: how a protein foundation model starts a protein

arXiv:2605. 16331v2 Announce Type: replace-cross Abstract: Protein language models are increasingly used to guide experimental and clinical decisions, yet it is often unclear whether a confident prediction reflects recognition of biological evidence or retrieval of a statistical default.

By Piotr Jedryszek, Oliver M. Crook

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arXiv AI

Jun 3

Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding

arXiv:2606. 02629v1 Announce Type: cross Abstract: Protein-protein interactions (PPIs) are essential for many biological processes.

By Zaifei Yang, Samuel Ping-Man Choi, James Kwok

rag multimodal benchmarks

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

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arXiv Machine Learning

Jun 10

Flexible Kernels for Protein Property Prediction

arXiv:2606. 11057v1 Announce Type: new Abstract: Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge.

By Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry N. Ward, Hunter Nisonoff, James M. McFarland, Gevorg Grigoryan

rag

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

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arXiv AI

Jul 28

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning

arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.

By Chen Wang, Boming Kang, Qinghua Cui

llms fine-tuning benchmarks

arXiv AI

Jun 9

Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation

arXiv:2606. 08191v1 Announce Type: cross Abstract: Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain.

By Kewei Li, Rongying Zhang, Xueli Wang, Xiwen Gong, Zhongjian Wang, Lan Huang, Ruochi Zhang, Fengfeng Zhou

computer-vision nlp

arXiv Machine Learning

Jul 3

CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data

arXiv:2607. 01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders.

By Jueqi Wang, Zachary Jacokes, John Darrell Van Horn, Kevin A. Pelphrey, Michael C. Schatz, Archana Venkataraman

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arXiv Machine Learning

Jun 16

Circuit Tracing in Autoregressive Protein Language Models

arXiv:2606. 16044v1 Announce Type: new Abstract: Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood.

By Darin Tsui, William Deinzer, Daniel Saeedi, Amirali Aghazadeh

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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

Jul 21

GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures

arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?

By Kseniia Vaniushkina, Jeongmin Lim, Jinyong Park

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