RRepoGEO

REPOGEO REPORT · LITE

jqtangust/Robust-R1

Default branch main · commit 02f2c9d3 · scanned 6/7/2026, 4:12:26 AM

GitHub: 533 stars · 17 forks

AI VISIBILITY SCORE
28 /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
2 / 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 jqtangust/Robust-R1, 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
    Add a concise, domain-specific opening sentence to the README

    Why:

    CURRENT
    This is the official repository for Robust-R1.
    COPY-PASTE FIX
    This repository presents Robust-R1, a novel framework for building robust multi-modal large language models (MM-LLMs) that excel in visual understanding despite input degradation.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the text of the MIT License, as suggested by the README link.
  • mediumreadme#3
    Add a 'Key Features' or 'What Robust-R1 Offers' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Key Features' or 'What Robust-R1 Offers') immediately after the initial description, detailing its unique contributions like 'Degradation-Aware Reasoning', 'Robust Visual Understanding', and 'Addressing isolated optimization between visual encoder and LLM'.

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 jqtangust/Robust-R1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 1×
  2. OpenCLIP · recommended 1×
  3. CLIPA · recommended 1×
  4. BLIP-2 · recommended 1×
  5. LLaVA · recommended 1×
  • CATEGORY QUERY
    How to build robust multi-modal large language models that handle visual input degradation effectively?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. OpenCLIP
    3. CLIPA
    4. BLIP-2
    5. LLaVA
    6. Fuyu-8B
    7. MiniGPT-4
    8. MiniGPT-v2
    9. Albumentations
    10. Torchvision Transforms
    11. Foolbox
    12. Advertorch
    13. Huber Loss
    14. Tukey's Biweight Loss

    AI recommended 14 alternatives but never named jqtangust/Robust-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for enhancing reasoning and interpretability in multi-modal AI against visual degradation?
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. Grad-CAM
    4. Captum
    5. InterpretML
    6. TensorFlow Explain (TFX)
    7. OpenCV

    AI recommended 7 alternatives but never named jqtangust/Robust-R1. 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 jqtangust/Robust-R1?
    pass
    AI named jqtangust/Robust-R1 explicitly

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

  • If a team adopts jqtangust/Robust-R1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jqtangust/Robust-R1 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 jqtangust/Robust-R1 solve, and who is the primary audience?
    pass
    AI did not name jqtangust/Robust-R1 — 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

Drop this badge into the README of jqtangust/Robust-R1. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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jqtangust/Robust-R1 — 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