RRepoGEO

REPOGEO REPORT · LITE

hemingkx/Awesome-Efficient-Reasoning

Default branch main · commit 2987edb5 · scanned 6/7/2026, 3:47:35 PM

GitHub: 889 stars · 45 forks

AI VISIBILITY SCORE
28 /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
2 / 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/Awesome-Efficient-Reasoning, 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 opening sentence to specify LLM research focus

    Why:

    CURRENT
    This repository contains a regularly updated paper list for **Efficient Reasoning**.
    COPY-PASTE FIX
    This repository contains a regularly updated paper list for **Efficient Reasoning in Large Language Models (LLMs)**, curated for researchers and practitioners.
  • mediumabout#2
    Update the repository description to include LLMs

    Why:

    CURRENT
    Paper list for Efficient Reasoning.
    COPY-PASTE FIX
    Curated paper list for Efficient Reasoning in Large Language Models (LLMs).

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/Awesome-Efficient-Reasoning
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. Hugging Face's transformers · recommended 1×
  5. Google's Speculative Decoding · recommended 1×
  • CATEGORY QUERY
    How can I make large language model reasoning more computationally efficient?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. GPTQ
    3. AWQ
    4. Hugging Face's transformers
    5. Google's Speculative Decoding
    6. Medusa
    7. vLLM
    8. TensorRT-LLM
    9. ONNX Runtime
    10. FlashAttention
    11. xFormers
    12. LoRA
    13. QLoRA
    14. AdaLoRA
    15. Apache TVM
    16. OpenXLA / XLA

    AI recommended 16 alternatives but never named hemingkx/Awesome-Efficient-Reasoning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are current research approaches for optimizing chain-of-thought prompting in LLMs?
    you: not recommended
    AI recommended (in order):
    1. Self-Refine
    2. Reflexion
    3. Constitutional AI
    4. Auto-CoT
    5. Active-CoT
    6. Least-to-Most Prompting
    7. Tree-of-Thought (ToT)
    8. Graph-of-Thought (GoT)
    9. CoT Distillation
    10. CoT Pruning/Compression
    11. Program-Aided Language Models (PAL)
    12. Toolformer
    13. Gorilla
    14. LLaMA-Adapter V2
    15. RLHF for CoT

    AI recommended 15 alternatives but never named hemingkx/Awesome-Efficient-Reasoning. 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/Awesome-Efficient-Reasoning?
    pass
    AI named hemingkx/Awesome-Efficient-Reasoning explicitly

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

  • If a team adopts hemingkx/Awesome-Efficient-Reasoning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hemingkx/Awesome-Efficient-Reasoning 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/Awesome-Efficient-Reasoning solve, and who is the primary audience?
    pass
    AI did not name hemingkx/Awesome-Efficient-Reasoning — 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/Awesome-Efficient-Reasoning — 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