RRepoGEO

REPOGEO REPORT · LITE

Visual-Agent/DeepEyes

Default branch main · commit 11d20c6b · scanned 6/26/2026, 9:22:38 AM

GitHub: 1,239 stars · 78 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)

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

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Visual-Agent/DeepEyes, 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
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    DeepEyes is an agentic multimodal model that learns to 'think with images' via end-to-end reinforcement learning, enabling complex visual reasoning, grounding, and problem-solving without direct supervision.
  • mediumreadme#2
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Add this paragraph immediately after the main H1 title (and before the 'Updates' section):

DeepEyes is an innovative agentic multimodal model designed to integrate visual information directly into its reasoning chain. It achieves this capability through end-to-end reinforcement learning, enabling advanced visual grounding, hallucination mitigation, and complex problem-solving without relying on supervised fine-tuning or specialized external models.

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 Visual-Agent/DeepEyes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch · recommended 2×
  3. OpenAI GPT-4V (Vision) · recommended 1×
  4. Google Gemini (Pro Vision / 1.5 Pro) · recommended 1×
  5. Llama 3 · recommended 1×
  • CATEGORY QUERY
    How can I develop an AI agent capable of integrating visual information directly into its reasoning chain?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. Google Gemini (Pro Vision / 1.5 Pro)
    3. Llama 3
    4. LLaVA (Large Language and Vision Assistant)
    5. Microsoft Copilot Studio
    6. Azure AI Services
    7. Azure OpenAI Service
    8. Hugging Face Transformers
    9. ViT (Vision Transformer)
    10. Llama 2
    11. Mistral
    12. Falcon
    13. LangChain
    14. LlamaIndex
    15. BLIP-2 (Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models)

    AI recommended 15 alternatives but never named Visual-Agent/DeepEyes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for training multimodal AI models using end-to-end reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow/Keras
    2. PyTorch
    3. Stable Baselines3
    4. RLlib
    5. Acme
    6. TensorFlow Probability
    7. PyTorch Distributions
    8. DreamerV3
    9. TensorFlow
    10. PyTorch
    11. Hugging Face Transformers
    12. OpenAI CLIP

    AI recommended 12 alternatives but never named Visual-Agent/DeepEyes. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Visual-Agent/DeepEyes?
    pass
    AI named Visual-Agent/DeepEyes explicitly

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

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

Subscribe to Pro for deep diagnoses

Visual-Agent/DeepEyes — 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