ARXIV:2602.17162 · GENOMIC AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks. While these paradigms excel at capturing local genomic syntax and fine-grained motif patterns, they often fail…

METHOD

Full abstract

Genomic Foundation Models (GFMs) have largely relied on Masked Language Modeling (MLM) or Next Token Prediction (NTP) to learn the language of life. While these paradigms excel at capturing local genomic syntax and fine-grained motif patterns, they often fail to capture the broader functional context, resulting in representations that lack a global biological perspective. We introduce JEPA-DNA, a novel pre-training framework that integrates the Joint-Embedding Predictive Architecture (JEPA) with traditional generative objectives. JEPA-DNA introduces latent grounding by coupling token-level recovery with a predictive objective in the latent space by supervising a CLS token. This forces the model to predict the high-level functional embeddings of masked genomic segments rather than focusing solely on individual nucleotides. JEPA-DNA extends both NTP and MLM paradigms and can be deployed either as a standalone from-scratch objective or as a continual pre-training enhancement for existing GFMs. Our evaluations across a diverse suite of genomic benchmarks demonstrate that JEPA-DNA consistently yields superior performance in supervised and zero-shot tasks compared to generative-only baselines. By providing a more robust and biologically grounded representation, JEPA-DNA offers a scalable path toward foundation models that understand not only the genomic alphabet, but also the underlying functional logic of the sequence.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our evaluations across a diverse suite of genomic benchmarks demonstrate that JEPA-DNA consistently yields superior performance in supervised and zero-shot tasks compared to generative-only…

WHY NOW

Genomic AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainJEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.17162 · GENOMIC AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Genomic AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainJEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.17162

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

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback27 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Genomic AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks. While these paradigms excel at capturing local genomic syntax and fine-grained motif patterns, they often fail to capture the broader functional context, resulting in representations that lack a global biological perspective.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Genomic Foundation Models (GFMs) have largely relied on Masked Language Modeling (MLM) or Next Token Prediction (NTP) to learn the language of life. While these paradigms excel at capturing local genomic syntax and fine-grained motif patterns, they often fail to capture the broader functional context, resulting in representations that lack a global biological perspective.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our evaluations across a diverse suite of genomic benchmarks demonstrate that JEPA-DNA consistently yields superior performance in supervised and zero-shot tasks compared to generative-only baselines.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Genomic AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Genomic AI

Competitors

not indexed

Competitive landscape

JEPA-DNA enhances genomic foundation models by introducing a novel joint-embedding predictive architecture for superior performance on genomic tasks.

Segment

Genomic AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.17162 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(27)

BioToken and BioFM – Biologically-Informed Tokenization Enables Accurate and Efficient Genomic Foundation Models

2025Aleksandr Medvedev, Karthik Viswanathan et al.

GeneJepa: A Predictive World Model of the Transcriptome

2025Elon Litman, Tyler Myers et al.

LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures

2025Hai Huang, Yann LeCun et al.

Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics

2025Gonzalo Benegas, Gökçen Eraslan et al.

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants

2025Gonzalo Benegas, Carlos Albors et al.

DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification

2024Minghao Yang, Zehua Wang et al.

The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics

2024Hugo Dalla-torre, Liam Gonzalez et al.

Genomic Language Models: Opportunities and Challenges

2024Gonzalo Benegas, Chengzhong Ye et al.

Sequence modeling and design from molecular to genome scale with Evo

2024Eric Nguyen, Michael Poli et al.

BEND: Benchmarking DNA Language Models on biologically meaningful tasks

2023Frederikke I. Marin, Felix Teufel et al.

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction

2023Gonzalo Benegas, Carlos Albors et al.

DNA language models are powerful predictors of genome-wide variant effects

2023Gonzalo Benegas, S. Batra et al.

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

2023Eric D Nguyen, Michael Poli et al.

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

2023Zhihan Zhou, Yanrong Ji et al.

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

2023Mahmoud Assran, Quentin Duval et al.

A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27

2022Yann LeCun, Courant

VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

2021Adrien Bardes, J. Ponce et al.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2019Jacob Devlin, Ming-Wei Chang et al.

Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly

2016Valerie A. Schneider, Tina A. Graves-Lindsay et al.

The Release 6 reference sequence of the Drosophila melanogaster genome

2015R. Hoskins, J. Carlson et al.

Showing 20 of 27 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkRepresentation Without Reward: A JEPA Audit for LLM Fine-Tuning

5.0

Extension

Builds On ThisProteinJEPA: Latent prediction complements protein language models

3.0

Builds On ThisLatentWave: JEPA Pretraining for Wireless Foundation Models

0.0

Builds On ThisLaya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction

4.0

Builds On ThisFF-JEPA: Long-Horizon Planning in World Models with Latent Planners

0.0

Builds On ThisBeyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion

0.0

Builds On ThisUWM-JEPA: Predictive World Models That Imagine in Belief Space

0.0

Commercially relevant

Higher ViabilityClin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories

7.0

Higher ViabilityThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model

7.0

Higher ViabilityCausal-JEPA: Learning World Models through Object-Level Latent Interventions

9.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.17162
Route: /paper/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures
Active tab: read
Artifact: jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures/paper-pack
REST build passport API/api/v1/paper/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "endpoints": {
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

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

Canonical ID jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures | Route /paper/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

MCP example

{
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  "arguments": {
    "arxiv_id": "2602.17162"
  }
}

source_context

{
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  "topic_slug": null,
  "benchmark_ref": null,
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}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

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

/buildability/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

Watchwatch

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

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

Paper ref

jepa-dna-grounding-genomic-foundation-models-through-joint-embedding-predictive-architectures

arXiv id

2602.17162

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

941dfaf6617a99a2be0df0ff0c1ab478e1ce036e38ce3d59736c1ca664ba8e1a

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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

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

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