REPOGEO REPORT · LITE

openai/sparse_attention

Default branch master · commit c53f3bdb · scanned 5/21/2026, 4:42:52 AM

GitHub: 1,613 stars · 191 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

30 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

3 / 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 openai/sparse_attention, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

sparse-attention, transformers, deep-learning, pytorch, cuda, attention-mechanisms, efficient-attention, machine-learning-primitives

highlicense#2

Add a LICENSE file and state the license in the README

Why:

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT or Apache-2.0, consult legal if unsure) and add a line to the README, for example: "This project is licensed under the MIT License – see the LICENSE file for details."

mediumreadme#3

Clarify the README's opening to highlight the repo's core offering as optimized primitives

Why:

CURRENT

This repository contains the sparse attention primitives used in Sparse Transformers (see blog and paper). Specifically, it includes the following: 1) A faster implementation of normal attention...

COPY-PASTE FIX

This repository provides highly optimized, low-level CUDA kernels and primitives for sparse attention, as used in Sparse Transformers (see blog and paper). It offers efficient implementations of strided and fixed attention, alongside a faster normal attention and a recompute decorator, designed to accelerate research into sparse attention mechanisms.

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 openai/sparse_attention

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

FlashAttention-2

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

FlashAttention-2 · recommended 2×
Longformer · recommended 2×
Reformer · recommended 2×
BigBird · recommended 2×
FlashAttention · recommended 1×

CATEGORY QUERY
How to efficiently process very long sequences using transformer models?
you: not recommended
AI recommended (in order):
1. FlashAttention
2. FlashAttention-2
3. Mistral 7B/8x7B
4. Llama 2/3
5. Gemma
6. Longformer
7. Reformer
8. BigBird
9. Performer
10. Linformer
11. Transformer-XL
12. XLNet
13. Mamba
14. Hierarchical Transformer
15. Long-T5
16. Compressive Transformer
AI recommended 16 alternatives but never named openai/sparse_attention. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are efficient implementations for sparse attention mechanisms in deep learning?
you: not recommended
AI recommended (in order):
1. FlashAttention-2
2. Longformer
3. BigBird
4. Reformer
5. PyTorch
6. TensorFlow
7. DeepSpeed
8. Xformers
AI recommended 8 alternatives but never named openai/sparse_attention. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
fail
Suggestion:
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 openai/sparse_attention?
pass
AI named openai/sparse_attention explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts openai/sparse_attention in production, what risks or prerequisites should they evaluate first?
pass
AI named openai/sparse_attention 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 openai/sparse_attention solve, and who is the primary audience?
pass
AI named openai/sparse_attention explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of openai/sparse_attention. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/openai/sparse_attention"><img src="https://repogeo.com/badge/openai/sparse_attention.svg" alt="RepoGEO" /></a>

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openai/sparse_attention — 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