Genomic Foundation Models

Latest papers

17 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

What DNA Foundation Models Learn Beyond Sequence Composition
Oct 11, 2026Vincenzo Yuto Civale, Andrew David Bagdanov, Alberto MagiGenomic Foundation Models Molecular Foundation Models
University of Florence
VANDAM: Viewing a nucleotide sequence with DNA molecular priors
Sep 30, 2026Jeremy Levy, Ariel Larey, Yury Nahshan +18Self-Supervised Pre-Training Genomic Foundation Models
Applied AI Architecture, NVIDIA, Israel. · Worldwide Field Ops, NVIDIA, Israel. · Developer Programs, NVIDIA, Israel. +3
GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEs
Sep 27, 2026Guang Yang, Fengchen LiuGenomic Foundation Models Distributed Inference
University of California, Los Angeles · University of California, Berkeley
Structure is not mechanism: high-gain gated-FFN rows across text and genomic foundation models
Sep 13, 2026Alexandros Tzanakakis, Aris Karatzikos, Ilias Georgakopoulos-SoaresGenomic Foundation Models Transformer FFNs
Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Dell Pediatric Research Institute, Austin, TX, USA · Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA · Department of Computer Science, College of Natural Sciences, The University of Texas at Austin, Austin, TX, USA
When Genomic Masking Priors Fail to Transfer: Strong Variant Prediction, Weak Functional Generation
Sep 4, 2026Susu Hu, Preetam Gattogi, Jens Lehmann +3Masked Diffusion Models Genomic Foundation Models
Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany · Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Germany · Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany +8
Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models
Jul 21, 2026Sarwan AliLLM Interpretability Genomics
Columbia University Irving Medical Center, New York, USA
DNA Language Models: An Assessment of Pre-Training for Fine-Tuning Tasks
Jun 29, 2026Romain Karpinsky, Julien Mozziconacci, Mickaël DelceyLanguage Model Pretraining Fine-Tuning
CNRS, LORIA · Nancy, 54000, France · CNRS +1
The Dark Regulome: Disentangling Predictability from Regulation in Genomic Foundation Models
Jun 5, 2026Chahat Baranwal, Aaditya Baranwal, Lakshya Nitin TandonGenomic Foundation Models Genomics
IIT Jodhpur · University of Central Florida · Northeastern University
LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
Jun 3, 2026Daria Ledneva, Denis KuznetsovAdaptive Tokenization Genomic Foundation Models
Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia.
GENEB: Why Genomic Models Are Hard to Compare
Jun 3, 2026Daria Ledneva, Mikhail Nuridinov, Denis KuznetsovBenchmark Design Genomic Foundation Models
Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia · Moscow State Institute of Steel and Alloys, Moscow, Russia
ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks
May 25, 2026Dongxin Ye, Fang Hu, Han Hu +6Distribution Shift Benchmark Design
Shanghai Innovation Institute Shanghai, China · University of Electronic Science and Technology of China Chengdu, China · Fudan University Shanghai, China +7
Entropy, Disagreement, and the Limits of Foundation Models in Genomics
Apr 5, 2026Maxime Rochkoulets, Lovro Vrček, Mile ŠikićGenomic Foundation Models Genomics
Genome Institute of Singapore, A*STAR, Singapore · KU Leuven, Belgium · Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
How Private Are DNA Embeddings? Inverting Foundation Model Representations of Genomic Sequences
Mar 6, 2026Sofiane Ouaari, Jules Kreuer, Nico PfeiferGenomic Foundation Models Model Inversion Attacks
Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Germany · Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Germany
Sparse autoencoders reveal organized biological knowledge but minimal regulatory logic in single-cell foundation models: a comparative atlas of Geneformer and scGPT
Mar 3, 2026Ihor KendiukhovGenomic Foundation Models Single-Cell Transcriptomics
Department of Computer Science, University of Tübingen, Tübingen, Germany
D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation
Mar 2, 2026Zhao Yang, Hengchang Liu, Chuan Cao +1Molecular Foundation Models Genomic Foundation Models
Gaoling School of Artificial Intelligence, Renmin University of China · Zhongguancun Academy
What Topological and Geometric Structure Do Biological Foundation Models Learn? Evidence from 141 Hypotheses
Feb 25, 2026Ihor KendiukhovNeural Representation Geometry Genomic Foundation Models
Department of Computer Science, University of Tübingen, Tübingen, Germany
JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
Feb 19, 2026Ariel Larey, Elay Dahan, Amit Bleiweiss +15Genomic Foundation Models Molecular Representation Learning
Applied AI Architecture, NVIDIA, Israel. · Worldwide Field Ops, NVIDIA, Israel. · Developer Programs, NVIDIA, Israel. +2

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What DNA Foundation Models Learn Beyond Sequence Composition

Vincenzo Yuto Civale, Andrew David Bagdanov, Alberto Magi

University of Florence

Genomic Foundation Models Molecular Foundation Models Bioinformatics Molecular Representation Learning Genomics Sequence Modeling

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Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition? We evaluate frozen representations from five DNA FMs against normalized k-mer frequencies across 57 genomic classification datasets and two exploratory gene-expression regression tasks. Simple k-mer features remain competitive with most models, whereas NTv3 shows a consistent advantage; the ranking is robust to globally fixed and multi-scale k-mer baselines and alternative downstream probes. To test incremental predictive utility directly, we compare global 6-mer features with their concatenation with each FM embedding under a matched Random Forest protocol. NTv3 improves 46 of 57 datasets (mean

Δ

MCC = +0.072), while Caduceus provides smaller gains and the other models do not improve mean MCC. We separately decompose embeddings into a component linearly predictable from 6-mer frequencies and a ridge residual, finding that linear recoverability does not reliably indicate downstream utility. Exact 6-mer-preserving shuffles further reveal task-relevant sequence-order sensitivity, most pronounced for NTv3, although order sensitivity alone does not guarantee predictive improvements. Our results establish compositional features as essential controls and offer complementary diagnostics for evaluating the added value of frozen genomic representations, without making claims about fine-tuning capacity.