RRepoGEO

REPOGEO REPORT · LITE

Zefan-Cai/R-KV

Default branch main · commit c8169f5f · scanned 5/24/2026, 1:13:02 PM

GitHub: 1,197 stars · 192 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 Zefan-Cai/R-KV, 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
    Reposition the README H1 to specify LLM KV cache compression

    Why:

    CURRENT
    <h1 align="center">R-KV</h1>
    COPY-PASTE FIX
    <h1 align="center">R-KV: Redundancy-aware KV Cache Compression for Reasoning Models</h1>
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root, e.g., with the MIT License text.
  • mediumtopics#3
    Expand repository topics to include compression and memory optimization

    Why:

    CURRENT
    kvcache, llm, reasoning-models
    COPY-PASTE FIX
    kvcache, llm, reasoning-models, compression, memory-optimization, llm-inference

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 Zefan-Cai/R-KV
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
bitsandbytes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. bitsandbytes · recommended 1×
  2. GPTQ · recommended 1×
  3. AWQ · recommended 1×
  4. SparseML · recommended 1×
  5. Hugging Face Optimum · recommended 1×
  • CATEGORY QUERY
    How can I reduce memory usage for large language model reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. GPTQ
    3. AWQ
    4. SparseML
    5. Hugging Face Optimum
    6. FlashAttention
    7. xFormers
    8. DeepSpeed
    9. Hugging Face Accelerate
    10. PEFT
    11. PyTorch

    AI recommended 11 alternatives but never named Zefan-Cai/R-KV. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking KV cache compression methods that maintain full accuracy for LLM inference.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    2. Hugging Face Optimum (huggingface/optimum)
    3. OpenVINO (openvinotoolkit/openvino)
    4. FlashAttention-2 (Dao-AILab/flash-attention)
    5. DeepSpeed-MII (microsoft/DeepSpeed-MII)
    6. LightSeq (bytedance/lightseq)
    7. Transformers (Hugging Face) (huggingface/transformers)
    8. vLLM (vllm-project/vllm)
    9. Triton Inference Server (triton-inference-server/server)
    10. LongFormer (allenai/longformer)
    11. BigBird (google-research/bigbird)

    AI recommended 11 alternatives but never named Zefan-Cai/R-KV. 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 Zefan-Cai/R-KV?
    pass
    AI named Zefan-Cai/R-KV explicitly

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

  • If a team adopts Zefan-Cai/R-KV in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Zefan-Cai/R-KV 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 Zefan-Cai/R-KV solve, and who is the primary audience?
    pass
    AI named Zefan-Cai/R-KV explicitly

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

Embed your GEO score

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Zefan-Cai/R-KV — 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