RRepoGEO

REPOGEO REPORT · LITE

Eclipsess/Awesome-Efficient-Reasoning-LLMs

Default branch main · commit c9e9e653 · scanned 6/1/2026, 12:12:53 PM

GitHub: 769 stars · 40 forks

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 Eclipsess/Awesome-Efficient-Reasoning-LLMs, 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's opening to clearly state it's a survey/awesome list

    Why:

    CURRENT
    # Awesome-Efficient-Reasoning-LLMs

[](https://arxiv.org/abs/2503.16419)

## [TMLR 2025] Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
    COPY-PASTE FIX
    # Awesome-Efficient-Reasoning-LLMs: A Curated Survey on Efficient Reasoning for Large Language Models

This repository serves as the official companion and curated awesome list for our TMLR 2025 paper, "Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models." It systematically investigates and organizes current progress in achieving efficient reasoning in LLMs.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or CC-BY-4.0 for content) in the repository root to clarify usage rights for the survey and curated list.
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    efficiency, large-language-models, large-reasoning-models
    COPY-PASTE FIX
    Add: survey, awesome-list, literature-review, llm-reasoning-efficiency

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 Eclipsess/Awesome-Efficient-Reasoning-LLMs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI's GPT-4 · recommended 1×
  4. Anthropic's Claude · recommended 1×
  5. FAISS · recommended 1×
  • CATEGORY QUERY
    How can I make large language models reason more efficiently without sacrificing accuracy?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI's GPT-4
    4. Anthropic's Claude
    5. FAISS
    6. ChromaDB
    7. Pinecone
    8. Haystack
    9. Hugging Face Transformers
    10. LoRA
    11. OpenAI Fine-tuning API
    12. Anthropic Claude 3 Opus/Sonnet
    13. Google Gemini 1.5 Pro
    14. Mistral Large
    15. Mixtral 8x7B
    16. Hugging Face Optimum
    17. bitsandbytes
    18. GPTQ

    AI recommended 18 alternatives but never named Eclipsess/Awesome-Efficient-Reasoning-LLMs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey on efficient reasoning for large language models?
    you: not recommended
    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 Eclipsess/Awesome-Efficient-Reasoning-LLMs?
    pass
    AI did not name Eclipsess/Awesome-Efficient-Reasoning-LLMs — 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 Eclipsess/Awesome-Efficient-Reasoning-LLMs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Eclipsess/Awesome-Efficient-Reasoning-LLMs 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 Eclipsess/Awesome-Efficient-Reasoning-LLMs solve, and who is the primary audience?
    pass
    AI did not name Eclipsess/Awesome-Efficient-Reasoning-LLMs — 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite