What it is
HyenaDNA replaced attention with implicit long convolutions (the Hyena operator) to model DNA at single-nucleotide resolution over up to a million tokens efficiently. The architecture directly influenced the StripedHyena backbone behind Evo.
Evidence trail
BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.
Model passport
How HyenaDNA represents biology
Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.
Biological scale
Modalities & tasks
Registry, claims and frontier intelligence
Version history not yet curated
1 version record · release year not yet normalized. Model-family identity remains separate from capability and access changes.
Explore version lineage →0 normalized claims
No task, dataset, split and metric claim has been normalized for this record yet.
Open claim intelligence →1 connected frontier
Genome design · Peer-reviewed capability
Inspect research horizon →Connected research frontiers
These records describe active research directions, not guaranteed capabilities of this model. Evidence stages and unresolved questions are preserved separately.
Genome-scale generative biology
Arc Institute · Stanford · NVIDIA · 2026-03-01Can a foundation model read, predict and design biological sequence continuously from single nucleotides to megabase-scale genomes?
Evidence boundary and unresolved questions
Generative plausibility is not equivalent to biological viability, function or safety. Long generated sequences require extensive synthesis, containment and functional review.
- What biological constraints are learned versus memorized?
- How should whole-genome designs be evaluated before synthesis?
- Can mechanistic interpretability keep pace with model scale?
genome foundation model · long context · sequence design · biosafetyOpen frontier record →Inputs and outputs
Inputs
DNA sequenceOutputs
Sequence predictionsEmbeddings or generated sequenceScientific and technical profile
Scientific principles
Technology
Scientific lineage
These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.
DNA as the hereditary transforming principle
Oswald Avery, Colin MacLeod & Maclyn McCartyGenomics, variant interpretation, gene therapy and sequence foundation models depend on DNA being the durable molecular carrier of biological information.
The DNA double helix and complementary base pairing
James Watson & Francis CrickSequence analysis, variant prediction, genome design and nucleic-acid therapeutics all rest on this structural logic.
Reading the sequences of proteins and DNA
Frederick SangerBiological foundation models exist because proteins and genomes became readable, comparable and computable at scale.
Information, entropy and communication
Claude E. ShannonSequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.
X-ray evidence for the helical structure of DNA
Rosalind Franklin & Raymond GoslingStructural genomics and sequence-to-structure reasoning began with experimentally grounded molecular geometry.
The central dogma and directional information transfer
Francis CrickMulti-omic models and sequence foundation models connect genotype, transcript and protein through this information-flow framework.
Evaluation evidence
BioAtlas has not yet extracted a structured benchmark claim for this record.
Known limitations
- Performance depends on the evaluation dataset and operating conditions.
- A structured benchmark claim has not yet been extracted for this record.
- Outputs require task-specific scientific and experimental validation.
Milestones
Architectural ancestor of Evo's StripedHyena.
From Chris Ré's Hazy Research lab.