RRepoGEO

REPOGEO REPORT · LITE

Fancy-MLLM/R1-Onevision

Default branch main · commit 976293f8 · scanned 6/17/2026, 12:57:27 AM

GitHub: 581 stars · 16 forks

AI VISIBILITY SCORE
28 /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
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 Fancy-MLLM/R1-Onevision, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Enhance README's opening to highlight the unique differentiator

    Why:

    CURRENT
    **R1-OneVision** is a versatile **multimodal reasoning large model**, designed to tackle complex visual reasoning tasks. It seamlessly integrates visual and textual data to offer precise interpretations of multimodal information, excelling in areas such as mathematics, science, deep image understanding, and logical reasoning. With its robust ability to perform multimodal reasoning, **R1-OneVision emerges as a powerful AI assistant capable of addressing a wide range of problem-solving challenges across different domains**.
    COPY-PASTE FIX
    **R1-OneVision** is a versatile **multimodal reasoning large model**, designed to tackle complex visual reasoning tasks. Its unique strength lies in its **cross-modal formalization pipeline**, which transforms images into formal textual representations to enable precise language-based reasoning. It seamlessly integrates visual and textual data to offer precise interpretations of multimodal information, excelling in areas such as mathematics, science, deep image understanding, and logical reasoning. With its robust ability to perform multimodal reasoning, **R1-OneVision emerges as a powerful AI assistant capable of addressing a wide range of problem-solving challenges across different domains**.
  • mediumhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2503.10615

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 Fancy-MLLM/R1-Onevision
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4o
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4o · recommended 2×
  2. Gemini 1.5 Pro · recommended 2×
  3. Claude 3 Opus · recommended 2×
  4. LLaVA · recommended 1×
  5. Fuyu-8B · recommended 1×
  • CATEGORY QUERY
    Looking for a visual language model capable of deep chain-of-thought reasoning across images.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. Claude 3 Opus
    4. LLaVA
    5. Fuyu-8B
    6. Qwen-VL-Max

    AI recommended 6 alternatives but never named Fancy-MLLM/R1-Onevision. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What models can perform advanced multimodal reasoning for scientific and mathematical problem-solving?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. Claude 3 Opus
    4. Llama 3
    5. Mathpix OCR
    6. GPT-4
    7. Gemini

    AI recommended 7 alternatives but never named Fancy-MLLM/R1-Onevision. 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 Fancy-MLLM/R1-Onevision?
    pass
    AI did not name Fancy-MLLM/R1-Onevision — 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 Fancy-MLLM/R1-Onevision in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Fancy-MLLM/R1-Onevision 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 Fancy-MLLM/R1-Onevision solve, and who is the primary audience?
    pass
    AI named Fancy-MLLM/R1-Onevision explicitly

    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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Fancy-MLLM/R1-Onevision — 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