RRepoGEO

REPOGEO REPORT · LITE

SUFE-AIFLM-Lab/Fin-R1

Default branch main · commit 06ead1e6 · scanned 6/15/2026, 7:23:00 AM

GitHub: 796 stars · 84 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 SUFE-AIFLM-Lab/Fin-R1, 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
  • hightopics#1
    Add specific topics for financial LLM and reasoning

    Why:

    COPY-PASTE FIX
    financial-llm, large-language-model, financial-reasoning, qwen, risk-control, esg-analysis, nlp, finance
  • highlicense#2
    Create a LICENSE file and clarify license in README

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, using the Apache-2.0 template. Additionally, add a clear statement like 'Fin-R1 is licensed under the Apache-2.0 License.' to the README's introductory section.
  • highreadme#3
    Add a prominent English summary to the main README

    Why:

    CURRENT
    <h1>Fin-R1：通过强化学习驱动的金融推理大模型</h1>
    COPY-PASTE FIX
    Insert the following English summary at the very beginning of the `README.md` file, before any existing content: `# Fin-R1: A Large Language Model for Complex Financial Reasoning` followed by: `Fin-R1 is a large language model for complex financial reasoning developed and open-sourced with the joint efforts of the SUFE-AIFLM-Lab at the School of Statistics and Data Science, Shanghai University of Finance and Economics and FinStep.AI. It specializes in financial code, calculations, risk control, and ESG analysis.`

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 SUFE-AIFLM-Lab/Fin-R1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BloombergGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. BloombergGPT · recommended 1×
  2. GPT-4 · recommended 1×
  3. GPT-4 Turbo · recommended 1×
  4. Claude 3 Opus · recommended 1×
  5. Llama 3 · recommended 1×
  • CATEGORY QUERY
    What are the best large language models for complex financial reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. BloombergGPT
    2. GPT-4
    3. GPT-4 Turbo
    4. Claude 3 Opus
    5. Llama 3
    6. Gemini 1.5 Pro

    AI recommended 6 alternatives but never named SUFE-AIFLM-Lab/Fin-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a cost-effective large language model for financial risk control and ESG analysis.
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 Turbo
    2. Llama 2
    3. Mistral 7B
    4. Cohere Command
    5. Google PaLM 2
    6. Falcon 7B / 40B
    7. Hugging Face

    AI recommended 7 alternatives but never named SUFE-AIFLM-Lab/Fin-R1. 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 SUFE-AIFLM-Lab/Fin-R1?
    pass
    AI named SUFE-AIFLM-Lab/Fin-R1 explicitly

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

  • If a team adopts SUFE-AIFLM-Lab/Fin-R1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SUFE-AIFLM-Lab/Fin-R1 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 SUFE-AIFLM-Lab/Fin-R1 solve, and who is the primary audience?
    pass
    AI did not name SUFE-AIFLM-Lab/Fin-R1 — 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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SUFE-AIFLM-Lab/Fin-R1 — 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