RRepoGEO

REPOGEO REPORT · LITE

HumanMLLM/R1-Omni

Default branch main · commit 17cafcae · scanned 5/16/2026, 5:32:51 PM

GitHub: 1,012 stars · 74 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 HumanMLLM/R1-Omni, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    R1-Omni is the industry's first application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-multimodal large language model, specifically for explainable emotion recognition using visual and audio modalities.
  • hightopics#2
    Add relevant GitHub topics

    Why:

    COPY-PASTE FIX
    ['multimodal-llm', 'emotion-recognition', 'reinforcement-learning', 'explainable-ai', 'omnimodal', 'audio-visual', 'large-language-models']
  • highlicense#3
    Add a LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root, for example, using the MIT License or Apache-2.0 License template.

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 HumanMLLM/R1-Omni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Prodigy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Prodigy · recommended 1×
  2. opencv/cvat · recommended 1×
  3. heartexlabs/label-studio · recommended 1×
  4. TadasBaltrusaitis/OpenFace · recommended 1×
  5. serengil/deepface · recommended 1×
  • CATEGORY QUERY
    How to build an explainable AI system for multimodal emotion recognition?
    you: not recommended
    AI recommended (in order):
    1. Prodigy
    2. CVAT (opencv/cvat)
    3. Label Studio (heartexlabs/label-studio)
    4. OpenFace (TadasBaltrusaitis/OpenFace)
    5. DeepFace (serengil/deepface)
    6. OpenSMILE
    7. LibROSA (librosa/librosa)
    8. Hugging Face Transformers (huggingface/transformers)
    9. spaCy (explosion/spaCy)
    10. PyTorch (pytorch/pytorch)
    11. TensorFlow/Keras (tensorflow/tensorflow)
    12. SHAP (shap/shap)
    13. LIME (marcotcr/lime)
    14. Grad-CAM
    15. Integrated Gradients
    16. Captum (pytorch/captum)

    AI recommended 16 alternatives but never named HumanMLLM/R1-Omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for combining reinforcement learning with LLMs for multimodal tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Google Gemini
    3. Anthropic Claude 3
    4. Google PaLM 2
    5. Hugging Face Transformers
    6. T5
    7. BART
    8. OpenAI CLIP
    9. DALL-E 3
    10. Google Vision AI
    11. Cloud Natural Language API
    12. Facebook DINOv2

    AI recommended 12 alternatives but never named HumanMLLM/R1-Omni. 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 HumanMLLM/R1-Omni?
    pass
    AI named HumanMLLM/R1-Omni explicitly

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

  • If a team adopts HumanMLLM/R1-Omni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HumanMLLM/R1-Omni 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 HumanMLLM/R1-Omni solve, and who is the primary audience?
    pass
    AI named HumanMLLM/R1-Omni explicitly

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

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HumanMLLM/R1-Omni — 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