RRepoGEO

REPOGEO REPORT · LITE

magic-research/PLLaVA

Default branch main · commit 6f49fd28 · scanned 6/1/2026, 7:38:17 PM

GitHub: 671 stars · 44 forks

AI VISIBILITY SCORE
30 /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
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 magic-research/PLLaVA, 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 to the repository

    Why:

    COPY-PASTE FIX
    ["video-dense-captioning", "video-llm", "multimodal-llm", "llava-extension", "video-understanding", "deep-learning", "computer-vision", "generative-ai"]
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with an appropriate open-source license (e.g., MIT, Apache-2.0, or a specific research license if applicable).
  • highreadme#3
    Add an introductory paragraph to the README clarifying PLLaVA's role

    Why:

    CURRENT
    The README immediately follows the title with project page links and SOTA tables, without a clear introductory paragraph for users.
    COPY-PASTE FIX
    Add the following paragraph immediately after the main title: "PLLaVA is an open-source multimodal large language model (MLLM) framework designed for advanced video understanding tasks, including video dense captioning and video question answering. It extends the LLaVA architecture to process video content efficiently, offering a parameter-free approach for researchers and developers to implement and build upon."

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 magic-research/PLLaVA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AWS Rekognition Video
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AWS Rekognition Video · recommended 2×
  2. Google Cloud Video AI · recommended 1×
  3. Azure Video Indexer · recommended 1×
  4. OpenAI's Whisper · recommended 1×
  5. Vidrovr · recommended 1×
  • CATEGORY QUERY
    How to generate detailed, dense captions for specific events within long video content?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Video AI
    2. AWS Rekognition Video
    3. Azure Video Indexer
    4. OpenAI's Whisper
    5. Vidrovr
    6. Trint
    7. Adobe Premiere Pro

    AI recommended 7 alternatives but never named magic-research/PLLaVA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a tool to perform multimodal understanding and answer questions directly from video streams.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Video Intelligence API
    2. Gemini Pro Vision
    3. PaLM 2
    4. Azure Video Analyzer
    5. Azure AI Services
    6. Azure Cognitive Services for Vision
    7. Azure Cognitive Services for Speech
    8. Azure OpenAI Service (GPT-4V)
    9. AWS Rekognition Video
    10. AWS AI Services
    11. AWS Transcribe
    12. Amazon Bedrock
    13. Claude 3
    14. Llama 2
    15. OpenAI's GPT-4V (GPT-4 with Vision)
    16. Hugging Face Transformers (huggingface/transformers)
    17. VideoMAE
    18. MViT
    19. Whisper (openai/whisper)
    20. BLIP-2
    21. LLaMA
    22. DeepMotion

    AI recommended 22 alternatives but never named magic-research/PLLaVA. 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 magic-research/PLLaVA?
    pass
    AI named magic-research/PLLaVA explicitly

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

  • If a team adopts magic-research/PLLaVA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named magic-research/PLLaVA 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 magic-research/PLLaVA solve, and who is the primary audience?
    pass
    AI named magic-research/PLLaVA explicitly

    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 magic-research/PLLaVA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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