RRepoGEO

REPOGEO REPORT · LITE

ximinng/LLM4SVG

Default branch master · commit 375d7c47 · scanned 6/3/2026, 1:53:06 PM

GitHub: 642 stars · 12 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 ximinng/LLM4SVG, 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
    Clarify project type in README's opening statement

    Why:

    CURRENT
    Official implementation for **"Empowering LLMs to Understand and Generate Complex Vector Graphics"**. This project enables Large Language Models to process, understand, and generate complex Scalable Vector Graphics (SVG).
    COPY-PASTE FIX
    This repository provides the official implementation and a comprehensive framework for **"Empowering LLMs to Understand and Generate Complex Vector Graphics"**. It enables Large Language Models to process, understand, and generate complex Scalable Vector Graphics (SVG), complete with datasets and evaluation tools.
  • mediumabout#2
    Enhance the repository description to emphasize its framework nature

    Why:

    CURRENT
    [CVPR 2025] Official implementation for "Empowering LLMs to Understand and Generate Complex Vector Graphics" https://arxiv.org/abs/2412.11102
    COPY-PASTE FIX
    [CVPR 2025] Official implementation and a comprehensive framework for training and evaluating LLMs to understand and generate complex vector graphics. https://arxiv.org/abs/2412.11102
  • lowtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    llm, llm-sft, llm-svg, llm4svg, svg-generation, svg-understanding, svgx-dataset, text-to-svg
    COPY-PASTE FIX
    llm, llm-sft, llm-svg, llm4svg, svg-generation, svg-understanding, svgx-dataset, text-to-svg, llm-framework, llm-benchmark, vector-graphics-llm

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 ximinng/LLM4SVG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 Opus · recommended 1×
  3. Llama 3 · recommended 1×
  4. Mistral · recommended 1×
  5. svgdotjs/svg.js · recommended 1×
  • CATEGORY QUERY
    How can I use large language models to generate complex SVG images from text descriptions?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Llama 3
    4. Mistral
    5. SVG.js (svgdotjs/svg.js)
    6. D3.js (d3/d3)
    7. CairoSVG (Kozea/CairoSVG)
    8. svglib (regebro/svglib)
    9. Mermaid (mermaid-js/mermaid)
    10. Graphviz (graphviz/graphviz)

    AI recommended 10 alternatives but never named ximinng/LLM4SVG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help train LLMs to understand and interpret scalable vector graphics data?
    you: not recommended
    AI recommended (in order):
    1. Inkscape
    2. BeautifulSoup
    3. lxml
    4. D3.js
    5. SVG.js
    6. Puppeteer
    7. OpenCV
    8. CairoSVG
    9. svglib
    10. Hugging Face Transformers
    11. PyTorch
    12. TensorFlow

    AI recommended 12 alternatives but never named ximinng/LLM4SVG. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 ximinng/LLM4SVG?
    pass
    AI named ximinng/LLM4SVG explicitly

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

  • If a team adopts ximinng/LLM4SVG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ximinng/LLM4SVG 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 ximinng/LLM4SVG solve, and who is the primary audience?
    pass
    AI named ximinng/LLM4SVG 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 ximinng/LLM4SVG. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/ximinng/LLM4SVG.svg)](https://repogeo.com/en/r/ximinng/LLM4SVG)
HTML
<a href="https://repogeo.com/en/r/ximinng/LLM4SVG"><img src="https://repogeo.com/badge/ximinng/LLM4SVG.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

ximinng/LLM4SVG — 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