RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/duo-attention

Default branch main · commit fe93c314 · scanned 6/14/2026, 10:28:17 PM

GitHub: 541 stars · 41 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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 mit-han-lab/duo-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
    llm, long-context, attention, kv-cache, inference-optimization, deep-learning, transformer-models, efficient-llm, machine-learning, ai
  • highreadme#2
    Add an explicit introductory sentence to the README

    Why:

    CURRENT
    [paper] [[slides](figures/DuoAttention.pdf)]
    COPY-PASTE FIX
    This repository presents DuoAttention, a novel framework designed to significantly reduce both pre-filling and decoding memory and latency for long-context Large Language Models (LLMs) by optimizing KV cache management.

[paper] [[slides](figures/DuoAttention.pdf)]
  • mediumreadme#3
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Existing Methods

(Describe how DuoAttention's unique approach, particularly its unified adaptive combination of sparse and kernel-based linear attention and its distinct retrieval and streaming heads for KV cache optimization, differentiates it from other LLM efficiency techniques such as StreamingLLM or LongRoPE.)

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 mit-han-lab/duo-attention
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
bitsandbytes
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. bitsandbytes · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. vLLM · recommended 2×
  4. StreamingLLM · recommended 2×
  5. LongRoPE · recommended 2×
  • CATEGORY QUERY
    How to reduce memory and latency for long-context LLM inference?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. AWQ
    3. GPTQ
    4. AutoGPTQ
    5. Hugging Face Transformers
    6. Google's Speculative Decoding
    7. Medusa
    8. vLLM
    9. NVIDIA TensorRT-LLM
    10. Hugging Face Text Generation Inference (TGI)
    11. PagedAttention
    12. FlashAttention-2
    13. StreamingLLM
    14. LongRoPE
    15. Mamba
    16. RetNet

    AI recommended 16 alternatives but never named mit-han-lab/duo-attention. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to optimize KV cache for efficient long-context LLM deployment.
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Hugging Face Transformers
    3. bitsandbytes
    4. DeepSpeed-MII
    5. LightLLM
    6. StreamingLLM
    7. LongRoPE
    8. Llama 2
    9. Mixtral 8x7B
    10. Gemma
    11. Hugging Face Accelerate

    AI recommended 11 alternatives but never named mit-han-lab/duo-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
    warn

    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 mit-han-lab/duo-attention?
    pass
    AI named mit-han-lab/duo-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 mit-han-lab/duo-attention in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mit-han-lab/duo-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 mit-han-lab/duo-attention solve, and who is the primary audience?
    pass
    AI named mit-han-lab/duo-attention explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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mit-han-lab/duo-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