RRepoGEO

REPOGEO REPORT · LITE

hemingkx/SpeculativeDecodingPapers

Default branch main · commit 9e6b2930 · scanned 5/19/2026, 7:32:57 PM

GitHub: 1,222 stars · 77 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
22 /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
1 / 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 hemingkx/SpeculativeDecodingPapers, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's main title to clarify its nature as a paper list

    Why:

    CURRENT
    <div align="center">
  <h2><i>Unlocking Efficiency in Large Language Model Inference:</i><br>A Comprehensive Survey of Speculative Decoding</h2> 
</div>
    COPY-PASTE FIX
    <div align="center">
  <h2><i>Speculative Decoding Papers:</i><br>A Curated List for Efficient LLM Inference</h2>
  <p>This repository provides a regularly updated, comprehensive collection of must-read papers and blogs on Speculative Decoding, designed for researchers and practitioners focused on accelerating Large Language Model (LLM) generation.</p>
</div>
  • mediumabout#2
    Refine the repository's 'About' description

    Why:

    CURRENT
    📰 Must-read papers and blogs on Speculative Decoding ⚡️
    COPY-PASTE FIX
    A comprehensive, regularly updated list of must-read research papers and blogs on Speculative Decoding for accelerating Large Language Model (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 hemingkx/SpeculativeDecodingPapers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TimDettmers/bitsandbytes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TimDettmers/bitsandbytes · recommended 1×
  2. microsoft/onnxruntime · recommended 1×
  3. huggingface/optimum · recommended 1×
  4. neuralmagic/sparseml · recommended 1×
  5. NVIDIA's Pruning Toolkit · recommended 1×
  • CATEGORY QUERY
    How can I improve the inference speed of large language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes (TimDettmers/bitsandbytes)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Hugging Face Optimum (huggingface/optimum)
    4. SparseML (neuralmagic/sparseml)
    5. NVIDIA's Pruning Toolkit
    6. Hugging Face Transformers (huggingface/transformers)
    7. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    8. OpenVINO (openvinotoolkit/openvino)
    9. DeepSpeed-MII (microsoft/DeepSpeed)
    10. Google's Draft-and-Verify
    11. PyTorch (pytorch/pytorch)
    12. flash_attn library (Dao-AILab/flash-attention)

    AI recommended 12 alternatives but never named hemingkx/SpeculativeDecodingPapers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest research advancements in accelerating LLM generation with speculative methods?
    you: not recommended
    AI recommended (in order):
    1. Medusa
    2. Lookahead Decoding
    3. DistilSpec
    4. SpecInfer
    5. vLLM
    6. Triton Inference Server

    AI recommended 6 alternatives but never named hemingkx/SpeculativeDecodingPapers. 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 hemingkx/SpeculativeDecodingPapers?
    pass
    AI did not name hemingkx/SpeculativeDecodingPapers — 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 hemingkx/SpeculativeDecodingPapers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hemingkx/SpeculativeDecodingPapers 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 hemingkx/SpeculativeDecodingPapers solve, and who is the primary audience?
    pass
    AI did not name hemingkx/SpeculativeDecodingPapers — 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?

Embed your GEO score

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hemingkx/SpeculativeDecodingPapers — 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
hemingkx/SpeculativeDecodingPapers — RepoGEO report