RRepoGEO

REPOGEO REPORT · LITE

yfzhang114/Thyme

Default branch main · commit bcf036f2 · scanned 6/11/2026, 1:43:14 PM

GitHub: 580 stars · 37 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 yfzhang114/Thyme, 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
    Reposition the core project description to the top of the README

    Why:

    CURRENT
    We are excited to introduce **Thyme: Think Beyond Images**. Thyme transcends traditional ``thinking with images'' paradigms by autonomously generating and executing diverse image processing and computational operations through executable code, significantly enhancing performance on high-resolution perception and complex reasoning tasks.
    COPY-PASTE FIX
    Thyme is a novel multimodal AI framework that transcends traditional image processing by autonomously generating and executing code for high-resolution perception and complex visual reasoning tasks. Leveraging a two-stage training strategy with supervised fine-tuning and reinforcement learning, empowered by the innovative GRPO-ATS algorithm, Thyme achieves a sophisticated balance between reasoning exploration and code execution precision.
  • mediumabout#2
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    ✨✨ [ICLR 2026] Think Beyond Images
    COPY-PASTE FIX
    A multimodal AI framework for high-resolution image perception and complex visual reasoning, generating and executing code for image processing tasks. [ICLR 2026]

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 yfzhang114/Thyme
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. OpenAI GPT-4 / GPT-3.5 Turbo · recommended 1×
  3. Google Gemini (Pro/Ultra) · recommended 1×
  4. Anthropic Claude 3 (Opus/Sonnet) · recommended 1×
  5. opencv/opencv · recommended 1×
  • CATEGORY QUERY
    How to build an AI that generates code for image processing and complex visual reasoning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Google Gemini (Pro/Ultra)
    3. Anthropic Claude 3 (Opus/Sonnet)
    4. OpenCV (opencv/opencv)
    5. scikit-image (scikit-image/scikit-image)
    6. TensorFlow / Keras (tensorflow/tensorflow)
    7. PyTorch (pytorch/pytorch)
    8. Hugging Face Transformers (huggingface/transformers)
    9. Weights & Biases (W&B)
    10. Google Cloud AI Platform
    11. AWS SageMaker
    12. Azure Machine Learning
    13. CLIP (openai/CLIP)
    14. Vision Transformers (ViT) / Swin Transformers

    AI recommended 14 alternatives but never named yfzhang114/Thyme. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for enhancing high-resolution image perception and complex reasoning with two-stage training?
    you: not recommended
    AI recommended (in order):
    1. Detectron2 (facebookresearch/detectron2)
    2. MMDetection (open-mmlab/mmdetection)
    3. PyTorch Lightning (Lightning-AI/lightning)
    4. Hugging Face Transformers (huggingface/transformers)
    5. TensorFlow Object Detection API (tensorflow/models)
    6. Keras (keras-team/keras)

    AI recommended 6 alternatives but never named yfzhang114/Thyme. 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 yfzhang114/Thyme?
    pass
    AI named yfzhang114/Thyme explicitly

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

  • If a team adopts yfzhang114/Thyme in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yfzhang114/Thyme 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 yfzhang114/Thyme solve, and who is the primary audience?
    pass
    AI named yfzhang114/Thyme 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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yfzhang114/Thyme — 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