RRepoGEO

REPOGEO REPORT · LITE

penghao-wu/vstar

Default branch main · commit 4ede6647 · scanned 5/31/2026, 11:03:42 AM

GitHub: 704 stars · 43 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 penghao-wu/vstar, 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
    Add a concise purpose statement to the README's introduction

    Why:

    CURRENT
    ### Paper | Project Page | Online Demo
    COPY-PASTE FIX
    V* introduces guided visual search as a core mechanism for multimodal LLMs, enabling them to perform precise visual grounding and reasoning. This repository provides its official PyTorch implementation.

### Paper | Project Page | Online Demo
  • mediumreadme#2
    Add a brief 'Why V*?' section to the README

    Why:

    CURRENT
    ## Contents:
    COPY-PASTE FIX
    ## Why V*?
While many multimodal LLMs exist, V* uniquely integrates a guided visual search mechanism, allowing for more precise visual grounding and reasoning by actively querying and processing specific visual information, rather than relying solely on static image embeddings.

## Contents:

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 penghao-wu/vstar
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenCLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenCLIP · recommended 2×
  2. BLIP · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. CLIP · recommended 1×
  5. ViT-GPT2 · recommended 1×
  • CATEGORY QUERY
    How to implement guided visual search as a core mechanism in multimodal LLMs using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. OpenCLIP
    2. Hugging Face Transformers
    3. CLIP
    4. BLIP
    5. ViT-GPT2
    6. Timm (PyTorch Image Models)
    7. BERT
    8. RoBERTa
    9. Faiss (Facebook AI Similarity Search)
    10. Weaviate
    11. Pinecone
    12. PyTorch's `nn.MultiheadAttention`
    13. Detectron2
    14. Llama 2
    15. Mistral
    16. GPT-2
    17. T5
    18. PyTorch Lightning

    AI recommended 18 alternatives but never named penghao-wu/vstar. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for open-source PyTorch projects that enhance multimodal LLMs with visual search capabilities.
    you: not recommended
    AI recommended (in order):
    1. OpenCLIP
    2. BLIP
    3. Flamingo
    4. ViLT
    5. ALBEF
    6. OFA

    AI recommended 6 alternatives but never named penghao-wu/vstar. 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 penghao-wu/vstar?
    pass
    AI named penghao-wu/vstar explicitly

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

  • If a team adopts penghao-wu/vstar in production, what risks or prerequisites should they evaluate first?
    pass
    AI named penghao-wu/vstar 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 penghao-wu/vstar solve, and who is the primary audience?
    pass
    AI named penghao-wu/vstar 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 penghao-wu/vstar. 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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HTML
<a href="https://repogeo.com/en/r/penghao-wu/vstar"><img src="https://repogeo.com/badge/penghao-wu/vstar.svg" alt="RepoGEO" /></a>
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penghao-wu/vstar — 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
penghao-wu/vstar — RepoGEO report