RRepoGEO

REPOGEO REPORT · LITE

HKUDS/SepLLM

Default branch main · commit f250f595 · scanned 6/5/2026, 10:56:54 PM

GitHub: 571 stars · 47 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 HKUDS/SepLLM, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a standard open-source license file, such as `LICENSE.md` with the MIT License or Apache-2.0 License text, to clarify usage rights for the project.
  • highabout#2
    Clarify the 'About' description to prevent AI misinterpretation

    Why:

    CURRENT
    [ICML 2025] "SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator"
    COPY-PASTE FIX
    SepLLM: A plug-and-play framework for accelerating Large Language Model inference through native sparse attention, compressing segments into separator tokens. [ICML 2025]
  • mediumtopics#3
    Expand repository topics for better query matching

    Why:

    CURRENT
    inference-speed, large-language-models, llms
    COPY-PASTE FIX
    inference-speed, large-language-models, llms, sparse-attention, llm-acceleration, model-compression, deep-learning-optimization

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 HKUDS/SepLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. microsoft/DeepSpeed · recommended 2×
  3. NVIDIA/TensorRT-LLM · recommended 1×
  4. vllm-project/vllm · recommended 1×
  5. openvinotoolkit/openvino · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large language model inference to reduce latency and cost?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    2. vLLM (vllm-project/vllm)
    3. DeepSpeed-MII (microsoft/DeepSpeed)
    4. OpenVINO (openvinotoolkit/openvino)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. Triton Inference Server (triton-inference-server/server)
    7. llama.cpp (ggerganov/llama.cpp)

    AI recommended 7 alternatives but never named HKUDS/SepLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What plug-and-play methods exist for optimizing LLM performance through sparse attention?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. FlashAttention-2 (Dao-AILab/flash-attention)
    3. LongRoPE
    4. NTK-RoPE
    5. xFormers (facebookresearch/xformers)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. PyTorch (pytorch/pytorch)
    8. Longformer (huggingface/transformers)
    9. BigBird (huggingface/transformers)

    AI recommended 9 alternatives but never named HKUDS/SepLLM. 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 HKUDS/SepLLM?
    pass
    AI named HKUDS/SepLLM explicitly

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

  • If a team adopts HKUDS/SepLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HKUDS/SepLLM 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 HKUDS/SepLLM solve, and who is the primary audience?
    pass
    AI named HKUDS/SepLLM 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 HKUDS/SepLLM. 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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HKUDS/SepLLM — 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