RRepoGEO

REPOGEO REPORT · LITE

zchuz/CoT-Reasoning-Survey

Default branch main · commit 835c10f2 · scanned 6/16/2026, 12:09:43 AM

GitHub: 501 stars · 16 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 zchuz/CoT-Reasoning-Survey, 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 README's opening to clarify repo's role as a survey resource

    Why:

    CURRENT
    This repository contains the resources for **ACL 2024** paper **_Navigate through Enigmatic Labyrinth, A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future_**
    COPY-PASTE FIX
    This repository serves as the official companion resource for our **ACL 2024** paper, '_A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future_'. It provides a comprehensive, categorized collection of papers, code links, and other materials related to Chain of Thought (CoT) reasoning, covering advances, frontiers, and future directions in deep learning and large language models.
  • mediumtopics#2
    Add more specific topics related to research surveys and correct typo

    Why:

    CURRENT
    chain-of-thought, chain-of-thought-reasoning, deep-learning, large-language-model, natural-language-procressing, survey-paper
    COPY-PASTE FIX
    chain-of-thought, chain-of-thought-reasoning, deep-learning, large-language-model, natural-language-processing, survey-paper, literature-review, research-survey, ai-reasoning, nlp-research
  • lowreadme#3
    Add a dedicated section detailing repository contents

    Why:

    COPY-PASTE FIX
    ## Repository Contents

This repository provides:
- A comprehensive, categorized reading list of Chain of Thought (CoT) reasoning papers.
- Links to relevant code implementations and datasets (where available).
- Regular updates on the latest research in CoT reasoning.
- Supplementary materials related to the ACL 2024 survey paper.

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 zchuz/CoT-Reasoning-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Self-Consistency
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Self-Consistency · recommended 1×
  2. Tree-of-Thought · recommended 1×
  3. Graph-of-Thoughts · recommended 1×
  4. Least-to-Most Prompting · recommended 1×
  5. Auto-CoT · recommended 1×
  • CATEGORY QUERY
    What are the latest breakthroughs in chain of thought reasoning for large language models?
    you: not recommended
    AI recommended (in order):
    1. Self-Consistency
    2. Tree-of-Thought
    3. Graph-of-Thoughts
    4. Least-to-Most Prompting
    5. Auto-CoT
    6. Retrieval-Augmented Generation
    7. Self-RAG
    8. Progressive-Hint Prompting
    9. Toolformer
    10. LLM Agents

    AI recommended 10 alternatives but never named zchuz/CoT-Reasoning-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a comprehensive survey of current research and future directions in deep learning reasoning methods.
    you: not recommended
    AI recommended (in order):
    1. Neuro-Symbolic AI: A Survey and Perspective" by Hitzler et al. (2022)
    2. Deep Learning for Symbolic Reasoning: A Survey" by Wang et al. (2021)
    3. Reasoning in Deep Learning: A Survey" by Zhang et al. (2020)
    4. Towards Neuro-Symbolic AI: A Survey of Approaches and Challenges" by Garcez et al. (2019)
    5. Explainable AI: A Survey of Current Trends and Future Challenges" by Adadi and Berrada (2018)
    6. A Survey of Deep Learning for Natural Language Processing" by Young et al. (2018)

    AI recommended 6 alternatives but never named zchuz/CoT-Reasoning-Survey. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 zchuz/CoT-Reasoning-Survey?
    pass
    AI did not name zchuz/CoT-Reasoning-Survey — 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 zchuz/CoT-Reasoning-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zchuz/CoT-Reasoning-Survey 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 zchuz/CoT-Reasoning-Survey solve, and who is the primary audience?
    pass
    AI did not name zchuz/CoT-Reasoning-Survey — 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?

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zchuz/CoT-Reasoning-Survey — 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