RRepoGEO

REPOGEO REPORT · LITE

hemingkx/SpeculativeDecodingPapers

Default branch main · commit b2625a16 · scanned 7/1/2026, 5:08:16 AM

GitHub: 1,263 stars · 81 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

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

OVERALL DIRECTION
  • highreadme#1
    Add a direct, one-line statement of purpose at the very top of the README

    Why:

    CURRENT
    The current README starts with a large survey title and author list before stating "This repository contains a regularly updated paper list...".
    COPY-PASTE FIX
    This repository curates and organizes research papers and blogs on Speculative Decoding for Large Language Model inference.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    speculative-decoding, llm-inference, large-language-models, nlp-research, paper-list, awesome-list
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://aclanthology.org/2024.findings-acl.456.pdf

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/transformers · recommended 1×
  4. NVIDIA/TensorRT · recommended 1×
  5. openvinotoolkit/openvino · recommended 1×
  • CATEGORY QUERY
    Looking for methods to accelerate large language model inference without sacrificing quality.
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes (TimDettmers/bitsandbytes)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Hugging Face Transformers (huggingface/transformers)
    4. NVIDIA TensorRT (NVIDIA/TensorRT)
    5. OpenVINO (openvinotoolkit/openvino)
    6. FlashAttention (Dao-AILab/flash-attention)
    7. DeepSpeed (microsoft/DeepSpeed)
    8. PyTorch (pytorch/pytorch)
    9. NVIDIA Apex (NVIDIA/apex)

    AI recommended 9 alternatives but never named hemingkx/SpeculativeDecodingPapers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find recent research on techniques to optimize LLM token generation speed?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Hugging Face Blog
    3. FlashAttention
    4. BetterTransformer
    5. Optimum library
    6. Google AI Blog
    7. DeepMind Blog
    8. Pathways
    9. JAX
    10. XLA
    11. Gemini
    12. PaLM
    13. Microsoft Research Blog
    14. Azure ML
    15. Turing NLG
    16. Meta AI Blog
    17. FAIR (Facebook AI Research) Papers
    18. Llama models
    19. NeurIPS
    20. ICML
    21. ICLR
    22. ACL
    23. EMNLP
    24. OpenReview
    25. ACL Anthology
    26. Papers With Code

    AI recommended 26 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