RRepoGEO

REPOGEO REPORT · LITE

PKU-YuanGroup/UniWorld

Default branch main · commit 2124cd68 · scanned 6/5/2026, 7:52:06 AM

GitHub: 875 stars · 29 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 PKU-YuanGroup/UniWorld, 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's opening to clearly state UniWorld's purpose

    Why:

    CURRENT
    The README starts with links and <h2>UniWorld-Family</h2> before any clear description.
    COPY-PASTE FIX
    Add a concise introductory paragraph immediately after the main title, e.g., "UniWorld is a VLM-Enhanced Unified Framework designed for high-resolution semantic encoding, visual understanding, and advanced image/video generation. It aims to bridge the gap between diverse visual tasks through a unified approach."
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository, containing the text of a standard open-source license (e.g., MIT License, Apache-2.0 License).
  • mediumabout#3
    Update the repository's "About" description for clarity

    Why:

    CURRENT
    UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
    COPY-PASTE FIX
    UniWorld: A VLM-Enhanced Unified Framework for High-Resolution Semantic Encoders, Visual Understanding, and Generation.

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 PKU-YuanGroup/UniWorld
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DALL-E 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DALL-E 3 · recommended 1×
  2. Midjourney · recommended 1×
  3. Stable Diffusion XL (SDXL) · recommended 1×
  4. Imagen (Google DeepMind) · recommended 1×
  5. Kandinsky 2.2 · recommended 1×
  • CATEGORY QUERY
    How to achieve unified visual understanding and high-resolution image generation from text?
    you: not recommended
    AI recommended (in order):
    1. DALL-E 3
    2. Midjourney
    3. Stable Diffusion XL (SDXL)
    4. Imagen (Google DeepMind)
    5. Kandinsky 2.2
    6. DeepFloyd IF

    AI recommended 6 alternatives but never named PKU-YuanGroup/UniWorld. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective high-resolution semantic encoders for image understanding and advanced editing?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. DINOv2
    3. Segment Anything Model (SAM)
    4. OpenCLIP
    5. OWL-ViT
    6. Mask2Former

    AI recommended 6 alternatives but never named PKU-YuanGroup/UniWorld. 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 PKU-YuanGroup/UniWorld?
    pass
    AI named PKU-YuanGroup/UniWorld explicitly

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

  • If a team adopts PKU-YuanGroup/UniWorld in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-YuanGroup/UniWorld 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 PKU-YuanGroup/UniWorld solve, and who is the primary audience?
    pass
    AI named PKU-YuanGroup/UniWorld 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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MARKDOWN (README)
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PKU-YuanGroup/UniWorld — 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