RRepoGEO

REPOGEO REPORT · LITE

om-ai-lab/VLM-R1

Default branch main · commit 67bc01f2 · scanned 5/13/2026, 1:02:35 AM

GitHub: 5,956 stars · 379 forks

AI VISIBILITY SCORE
35 /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
3 / 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 om-ai-lab/VLM-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
  • highreadme#1
    Reposition the README H1 to clarify core differentiator

    Why:

    CURRENT
    # VLM-R1: A stable and generalizable R1-style Large Vision-Language Model
    COPY-PASTE FIX
    # VLM-R1: A Reinforcement Learning approach to R1-style Large Vision-Language Models for superior out-of-domain generalization in visual understanding tasks.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    deepseek-r1, grpo, llm, multimodal, multimodal-r1, qwen, r1-zero, reinforcement-learning, vlm, vlm-r1
    COPY-PASTE FIX
    deepseek-r1, grpo, llm, multimodal, multimodal-r1, qwen, r1-zero, reinforcement-learning, vlm, vlm-r1, reinforced-vlm, visual-understanding
  • mediumabout#3
    Refine the repository description for clarity on R1-style reinforcement learning

    Why:

    CURRENT
    Solve Visual Understanding with Reinforced VLMs
    COPY-PASTE FIX
    Achieve stable and generalizable visual understanding using R1-style Large Vision-Language Models with reinforcement learning.

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 om-ai-lab/VLM-R1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 1×
  2. BLIP-2 · recommended 1×
  3. ViLT · recommended 1×
  4. Flamingo · recommended 1×
  5. LLaVA · recommended 1×
  • CATEGORY QUERY
    What are good approaches for integrating vision and language models for robust visual understanding?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. BLIP-2
    3. ViLT
    4. Flamingo
    5. LLaVA
    6. OFA
    7. CoCa

    AI recommended 7 alternatives but never named om-ai-lab/VLM-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a generalizable vision-language model leveraging reinforcement learning for complex visual tasks.
    you: not recommended
    AI recommended (in order):
    1. CLIP (openai/CLIP)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Ray RLlib (ray-project/ray)
    4. PaLM-E
    5. Gato
    6. Data2vec 2.0 (facebookresearch/data2vec)
    7. Florence
    8. Hugging Face Transformers (huggingface/transformers)
    9. ViT-GPT2
    10. BLIP (salesforce/BLIP)

    AI recommended 10 alternatives but never named om-ai-lab/VLM-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
    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 om-ai-lab/VLM-R1?
    pass
    AI named om-ai-lab/VLM-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 om-ai-lab/VLM-R1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named om-ai-lab/VLM-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 om-ai-lab/VLM-R1 solve, and who is the primary audience?
    pass
    AI named om-ai-lab/VLM-R1 explicitly

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

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om-ai-lab/VLM-R1 — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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