RRepoGEO

REPOGEO REPORT · LITE

RUCAIBox/Slow_Thinking_with_LLMs

Default branch main · commit 834e1b79 · scanned 6/7/2026, 7:08:17 AM

GitHub: 765 stars · 41 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 RUCAIBox/Slow_Thinking_with_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 clarify its purpose and audience

    Why:

    CURRENT
    We are STILL exploring the uncharted territory of o1-like reasoning systems.
    COPY-PASTE FIX
    This repository compiles research reports, models, and benchmarks focused on enhancing 'slow thinking' and advanced reasoning in Large Language Models (LLMs). It serves as a hub for researchers and practitioners exploring novel LLM reasoning mechanisms, problem-solving, and evaluation.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm-reasoning, slow-thinking, ai-research, llm-benchmarks, code-reasoning, mathematical-reasoning, reinforcement-learning, multi-modal-llm
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root to clarify usage terms for the research and code.

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 RUCAIBox/Slow_Thinking_with_LLMs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. OpenAI API · recommended 1×
  4. Anthropic API · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to enhance large language model reasoning capabilities for complex problem-solving?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI API
    4. Anthropic API
    5. Pinecone
    6. Weaviate (weaviate/weaviate)
    7. ChromaDB (chroma-core/chroma)
    8. Jupyter notebooks (jupyter/notebook)
    9. OpenAI's Function Calling API
    10. Hugging Face Transformers (huggingface/transformers)
    11. Google Cloud Vertex AI
    12. AWS SageMaker

    AI recommended 12 alternatives but never named RUCAIBox/Slow_Thinking_with_LLMs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for evaluating and improving LLM performance on complex mathematical or coding challenges?
    you: not recommended
    AI recommended (in order):
    1. HumanEval
    2. MBPP
    3. Codeforces
    4. LeetCode
    5. HackerRank
    6. OpenAI Evals
    7. DeepMind's AlphaCode
    8. Pyserini
    9. Anserini
    10. SymPy
    11. Wolfram Alpha API
    12. unittest
    13. pytest
    14. Jest
    15. Mocha

    AI recommended 15 alternatives but never named RUCAIBox/Slow_Thinking_with_LLMs. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 RUCAIBox/Slow_Thinking_with_LLMs?
    pass
    AI named RUCAIBox/Slow_Thinking_with_LLMs explicitly

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

  • If a team adopts RUCAIBox/Slow_Thinking_with_LLMs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named RUCAIBox/Slow_Thinking_with_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 RUCAIBox/Slow_Thinking_with_LLMs solve, and who is the primary audience?
    pass
    AI did not name RUCAIBox/Slow_Thinking_with_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

Drop this badge into the README of RUCAIBox/Slow_Thinking_with_LLMs. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/RUCAIBox/Slow_Thinking_with_LLMs.svg)](https://repogeo.com/en/r/RUCAIBox/Slow_Thinking_with_LLMs)
HTML
<a href="https://repogeo.com/en/r/RUCAIBox/Slow_Thinking_with_LLMs"><img src="https://repogeo.com/badge/RUCAIBox/Slow_Thinking_with_LLMs.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

RUCAIBox/Slow_Thinking_with_LLMs — 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