RRepoGEO

REPOGEO REPORT · LITE

om-ai-lab/VLM-R1

Default branch main · commit 67bc01f2 · scanned 6/23/2026, 9:47:35 AM

GitHub: 5,988 stars · 381 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
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 README H1 to highlight specific model and approach

    Why:

    CURRENT
    # VLM-R1: A stable and generalizable R1-style Large Vision-Language Model
    COPY-PASTE FIX
    # VLM-R1: Achieve State-of-the-Art Referring Expression Comprehension and Object Detection with our Stable and Generalizable R1-style Large Vision-Language Model (VLM) trained via Reinforcement Learning.
  • mediumhomepage#2
    Add a homepage URL to the About section

    Why:

    COPY-PASTE FIX
    https://om-ai-lab.github.io/VLM-R1-project
  • mediumreadme#3
    Add a clarifying sentence about the repo's nature

    Why:

    COPY-PASTE FIX
    This repository provides the official open-source implementation and pre-trained checkpoints for VLM-R1, a novel R1-style Large Vision-Language Model.

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
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/trl · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. DLR-RM/stable-baselines3 · recommended 1×
  5. deepmind/acme · recommended 1×
  • CATEGORY QUERY
    How can I build a stable and generalizable vision-language model using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL (huggingface/trl)
    3. PyTorch (pytorch/pytorch)
    4. Stable-Baselines3 (DLR-RM/stable-baselines3)
    5. Acme (deepmind/acme)
    6. OpenAI Gym (openai/gym)
    7. Farama Foundation Gymnasium (Farama-Foundation/Gymnasium)
    8. TensorFlow (tensorflow/tensorflow)
    9. RLlib (ray-project/ray)

    AI recommended 9 alternatives but never named om-ai-lab/VLM-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which multimodal large language models excel at referring expression comprehension and object detection?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. OWL-ViT
    3. GLIP
    4. MDETR
    5. Kosmos-2

    AI recommended 5 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.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite