RRepoGEO

REPOGEO REPORT · LITE

HumanMLLM/R1-Omni

Default branch main · commit 17cafcae · scanned 6/27/2026, 3:08:21 PM

GitHub: 1,016 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

2 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 for the repository

    Why:

    COPY-PASTE FIX
    R1-Omni applies Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-multimodal large language model for explainable emotion recognition using visual and audio cues.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clearly state the project's licensing terms.

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
Acme
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Acme · recommended 1×
  2. Captum · recommended 1×
  3. LIME · recommended 1×
  4. SHAP · recommended 1×
  5. RLlib · recommended 1×
  • CATEGORY QUERY
    How to build explainable AI for multimodal emotion recognition using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Acme
    2. Captum
    3. LIME
    4. SHAP
    5. RLlib
    6. InterpretML
    7. TensorFlow Agents
    8. TensorFlow Explainability (TF-X)
    9. PyTorch-Ignite
    10. Grad-CAM
    11. Score-CAM
    12. Gymnasium
    13. OpenAI Gym

    AI recommended 13 alternatives but never named HumanMLLM/R1-Omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools integrate large language models for processing combined visual and audio emotional cues?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. transformers
    3. 🤗 Transformers Agents
    4. OpenCV
    5. DeepFace
    6. FaceNet
    7. librosa
    8. SpeechBrain
    9. PyTorch
    10. TensorFlow
    11. OpenAI API
    12. GPT-4o
    13. GPT-4V
    14. Whisper
    15. Praat
    16. OpenSMILE
    17. Google Cloud AI Platform
    18. Vertex AI
    19. MediaPipe
    20. Google Speech-to-Text API
    21. Azure AI Services
    22. Azure OpenAI Service
    23. Azure AI Vision
    24. Azure AI Speech
    25. MMDetection

    AI recommended 25 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