RRepoGEO

REPOGEO REPORT · LITE

jeffhj/LM-reasoning

Default branch main · commit bfdadac2 · scanned 6/5/2026, 2:37:50 PM

GitHub: 571 stars · 37 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 jeffhj/LM-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

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 clarify its nature as a curated GitHub resource

    Why:

    CURRENT
    # Reasoning in Large Language Models

This repository contains a collection of papers and resources on Reasoning in Large Language Models.
    COPY-PASTE FIX
    # Awesome Reasoning in Large Language Models: A Curated Collection of Papers & Resources

This GitHub repository serves as a continuously updated, community-contributable collection of academic papers and practical resources focused on Reasoning in Large Language Models (LLMs).
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/jeffhj/LM-reasoning
  • lowreadme#3
    Clarify the README's scope beyond a single survey

    Why:

    CURRENT
    For more details, please refer to Towards Reasoning in Large Language Models: A Survey
    COPY-PASTE FIX
    This repository is an evolving collection, building upon the foundational work presented in "Towards Reasoning in Large Language Models: A Survey" and continuously incorporating new research and resources.

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 jeffhj/LM-reasoning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv · recommended 1×
  2. Google Scholar · recommended 1×
  3. ACL Anthology · recommended 1×
  4. NeurIPS · recommended 1×
  5. ICML · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers and resources on improving reasoning in large language models?
    you: not recommended
    AI recommended (in order):
    1. arXiv
    2. Google Scholar
    3. ACL Anthology
    4. NeurIPS
    5. ICML
    6. ICLR
    7. Papers With Code
    8. Distill.pub
    9. Hugging Face

    AI recommended 9 alternatives but never named jeffhj/LM-reasoning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective prompt engineering techniques for enhancing complex reasoning abilities in language models?
    you: not recommended
    AI recommended (in order):
    1. Chain-of-Thought (CoT) Prompting
    2. Self-Consistency
    3. Tree-of-Thought (ToT) Prompting
    4. Retrieval-Augmented Generation (RAG)
    5. Program-Aided Language Models (PAL)
    6. Generated Knowledge Prompting

    AI recommended 6 alternatives but never named jeffhj/LM-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 jeffhj/LM-reasoning?
    pass
    AI named jeffhj/LM-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 jeffhj/LM-reasoning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jeffhj/LM-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 jeffhj/LM-reasoning solve, and who is the primary audience?
    pass
    AI named jeffhj/LM-reasoning explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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