RRepoGEO

REPOGEO REPORT · LITE

zhaochen0110/Awesome_Think_With_Images

Default branch main · commit 0b4f782d · scanned 5/22/2026, 1:03:33 PM

GitHub: 1,458 stars · 45 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
15 /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
0 / 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 zhaochen0110/Awesome_Think_With_Images, 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 H1 to explicitly state it's a curated list of resources

    Why:

    CURRENT
    # 🧠🤖 Awesome-Think-With-Images
    COPY-PASTE FIX
    # 🧠🤖 Awesome-Think-With-Images: A Curated List of Papers and Resources
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of a standard open-source license (e.g., MIT License).
  • mediumtopics#3
    Add a topic to reinforce its nature as a collection of research papers

    Why:

    CURRENT
    large-vision-language-models, multimodal-reasoning-visual-reasoning, survey-awesome-list, thinking-with-images
    COPY-PASTE FIX
    large-vision-language-models, multimodal-reasoning-visual-reasoning, survey-awesome-list, thinking-with-images, research-papers

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 zhaochen0110/Awesome_Think_With_Images
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. CVPR · recommended 1×
  4. ICCV · recommended 1×
  5. ECCV · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive research on leveraging visual information for multimodal AI reasoning?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. CVPR
    4. ICCV
    5. ECCV
    6. IEEE Xplore Digital Library
    7. NeurIPS
    8. ICML
    9. ACL
    10. EMNLP
    11. Papers With Code
    12. Distill.pub

    AI recommended 12 alternatives but never named zhaochen0110/Awesome_Think_With_Images. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key approaches for enabling dynamic visual information processing in large multimodal models?
    you: not recommended
    AI recommended (in order):
    1. VideoMAE
    2. MViT
    3. Timesformer
    4. OmniSource
    5. PyTorch's `torch.nn.LSTM`
    6. PyTorch's `torch.nn.GRU`
    7. TensorFlow's `tf.keras.layers.LSTM`
    8. TensorFlow's `tf.keras.layers.GRU`
    9. Perceiver IO
    10. ViViT
    11. Flamingo
    12. Gato
    13. PaLM-E
    14. Instant-NGP
    15. DreamFusion
    16. Plenoxels
    17. Avalanche
    18. Learn-to-Grow
    19. Stable Baselines3
    20. Ray RLlib

    AI recommended 20 alternatives but never named zhaochen0110/Awesome_Think_With_Images. 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 zhaochen0110/Awesome_Think_With_Images?
    pass
    AI did not name zhaochen0110/Awesome_Think_With_Images — likely talking about a different project

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

  • If a team adopts zhaochen0110/Awesome_Think_With_Images in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name zhaochen0110/Awesome_Think_With_Images — likely talking about a different project

    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 zhaochen0110/Awesome_Think_With_Images solve, and who is the primary audience?
    pass
    AI did not name zhaochen0110/Awesome_Think_With_Images — likely talking about a different project

    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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zhaochen0110/Awesome_Think_With_Images — 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