RRepoGEO

REPOGEO REPORT · LITE

LightChen233/Awesome-Long-Chain-of-Thought-Reasoning

Default branch main · commit e6b2d948 · scanned 6/11/2026, 2:03:08 AM

GitHub: 637 stars · 32 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 LightChen233/Awesome-Long-Chain-of-Thought-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
  • highabout#1
    Refine the 'About' description to clarify repo type

    Why:

    CURRENT
    Latest Advances on Long Chain-of-Thought Reasoning
    COPY-PASTE FIX
    A curated collection of the latest advances, papers, and resources on Long Chain-of-Thought (LCoT) Reasoning for Large Language Models.
  • highlicense#2
    Add a LICENSE file or clarify licensing in README

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0) to the repository root, or explicitly state the licensing terms in the README, for example: 'This repository is licensed under the [License Name] License. See the [LICENSE file/section] for details.'
  • mediumreadme#3
    Strengthen the README's opening to emphasize 'Awesome List' nature

    Why:

    CURRENT
    Welcome to the repository associated with our survey paper, 'Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models'. This repository contains **resources and updates** related to our ongoing Long CoT research.
    COPY-PASTE FIX
    Welcome to **Awesome-Long-Chain-of-Thought-Reasoning**, a comprehensive and curated collection of resources and the latest advances related to our survey paper, 'Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models'. This repository serves as a living compendium of ongoing Long CoT research.

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 LightChen233/Awesome-Long-Chain-of-Thought-Reasoning
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome-CoT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-CoT · recommended 1×
  2. Google AI Blog · recommended 1×
  3. Google Scholar · recommended 1×
  4. OpenAI Blog · recommended 1×
  5. Hugging Face Blog · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources on long chain-of-thought reasoning for LLMs?
    you: not recommended
    AI recommended (in order):
    1. Awesome-CoT
    2. Google AI Blog
    3. Google Scholar
    4. OpenAI Blog
    5. Hugging Face Blog
    6. Transformers library (huggingface/transformers)
    7. arXiv.org
    8. Papers With Code

    AI recommended 8 alternatives but never named LightChen233/Awesome-Long-Chain-of-Thought-Reasoning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to enhance large language model reasoning capabilities using advanced thinking processes?
    you: not recommended
    AI recommended (in order):
    1. Chain-of-Thought (CoT) Prompting
    2. Standard CoT
    3. Zero-shot CoT
    4. Few-shot CoT
    5. Self-Consistency (CoT-SC)
    6. Tree-of-Thought (ToT)
    7. Reflexion
    8. Program-Aided Language Models (PAL)
    9. ReAct (Reasoning and Acting)
    10. Retrieval-Augmented Generation (RAG)
    11. Self-Correction/Self-Refinement

    AI recommended 11 alternatives but never named LightChen233/Awesome-Long-Chain-of-Thought-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 LightChen233/Awesome-Long-Chain-of-Thought-Reasoning?
    pass
    AI did not name LightChen233/Awesome-Long-Chain-of-Thought-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?

  • If a team adopts LightChen233/Awesome-Long-Chain-of-Thought-Reasoning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LightChen233/Awesome-Long-Chain-of-Thought-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 LightChen233/Awesome-Long-Chain-of-Thought-Reasoning solve, and who is the primary audience?
    pass
    AI did not name LightChen233/Awesome-Long-Chain-of-Thought-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?

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LightChen233/Awesome-Long-Chain-of-Thought-Reasoning — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite