REPOGEO REPORT · LITE

HazyResearch/hyena-dna

Default branch main · commit d553021b · scanned 5/30/2026, 2:33:07 PM

GitHub: 786 stars · 107 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface HazyResearch/hyena-dna, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1
Clarify README H1 to explicitly state official implementation
Why:
CURRENT
```
# HyenaDNA
```
COPY-PASTE FIX
```
# HyenaDNA: Official Implementation for Long-Range Genomic Foundation Models
```

mediumreadme#2

Strengthen README intro to highlight pretraining/fine-tuning for genomics

Why:

CURRENT

Welcome to the HyenaDNA repo! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to \1 million tokens**\* at \single nucleotide resolution**\*. The repo is a work in progress, but we're very excited to get this in the hands of researchers, so bare with us :) This repo is best suited for those who want to pretrain a HyenaDNA model, or try one of the downstream tasks from the paper.

COPY-PASTE FIX

Welcome to the **official HyenaDNA repository**, providing the implementation for our long-range genomic foundation model. HyenaDNA is pretrained on context lengths of up to 1 million tokens at single nucleotide resolution, making it ideal for advanced genomics research. This repository is specifically designed for researchers and practitioners looking to **pretrain a HyenaDNA model** or **fine-tune it for various downstream genomic tasks**.

lowreadme#3

Add a 'Comparison with Alternatives' section to README

Why:

COPY-PASTE FIX

## Comparison with Alternatives

HyenaDNA distinguishes itself from models like GenSLM, Nucleotide Transformer, and DNABERT by leveraging the Hyena architecture. This enables it to efficiently process significantly longer DNA sequences (up to millions of base pairs) with sub-quadratic computational complexity, overcoming the quadratic scaling limitations of standard Transformer models and allowing for more comprehensive genomic analysis.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface HazyResearch/hyena-dna

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

HyenaDNA

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

HyenaDNA · recommended 1×
GenSLM · recommended 1×
Nucleotide Transformer · recommended 1×
DNABERT · recommended 1×
LongNet · recommended 1×

CATEGORY QUERY
What foundation models are available for analyzing extremely long genomic sequences?
you: not recommended
AI recommended (in order):
1. HyenaDNA
2. GenSLM
3. Nucleotide Transformer
4. DNABERT
5. LongNet
6. Perceiver IO
7. BigBird
AI recommended 7 alternatives but never named HazyResearch/hyena-dna. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I pretrain or fine-tune a large language model for genomics research?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch Lightning
3. DeepSpeed
4. JAX
5. Flax
6. NVIDIA NeMo
7. TensorFlow
8. Keras
9. OpenFold
10. AlphaFold
AI recommended 10 alternatives but never named HazyResearch/hyena-dna. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of HazyResearch/hyena-dna?
pass
AI did not name HazyResearch/hyena-dna — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts HazyResearch/hyena-dna in production, what risks or prerequisites should they evaluate first?
pass
AI named HazyResearch/hyena-dna explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo HazyResearch/hyena-dna solve, and who is the primary audience?
pass
AI named HazyResearch/hyena-dna explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HazyResearch/hyena-dna — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite