RRepoGEO

REPOGEO REPORT · LITE

StarsfieldAI/R1-V

Default branch main · commit e35f97e5 · scanned 6/19/2026, 10:36:51 PM

GitHub: 4,060 stars · 283 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
23 /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
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 StarsfieldAI/R1-V, 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
    Update the repository description to clearly state its purpose

    Why:

    CURRENT
    Witness the aha moment of VLM with less than $3.
    COPY-PASTE FIX
    A framework and codebase for reinforcing super generalization ability in Vision-Language Models (VLMs) using reinforcement learning, focusing on cost-effective visual reasoning agents.
  • 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 the Apache-2.0 license.

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 StarsfieldAI/R1-V
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
danijar/dreamerv3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. danijar/dreamerv3 · recommended 2×
  2. tensorflow/tensorflow · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. huggingface/transformers · recommended 1×
  5. huggingface/trl · recommended 1×
  • CATEGORY QUERY
    How to improve vision-language model generalization efficiently using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face `trl` (huggingface/trl)
    3. OpenAI CLIP (openai/CLIP)
    4. DreamerV3 (danijar/dreamerv3)
    5. TensorFlow (tensorflow/tensorflow)
    6. PyTorch (pytorch/pytorch)
    7. `torch.distributions` (pytorch/pytorch)
    8. `tf.distributions` (tensorflow/tensorflow)
    9. `pycocoevalcap` (tylin/coco-caption)
    10. `Stable-Baselines3` (DLR-RM/stable-baselines3)
    11. Diffusers (huggingface/diffusers)
    12. Argilla (argilla-io/argilla)
    13. Scale AI

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

    Show full AI answer
  • CATEGORY QUERY
    What are cost-effective methods for developing visual reasoning agents using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. ResNet
    3. MobileNetV2
    4. PyTorch Image Models (timm)
    5. TensorFlow Hub
    6. RLlib
    7. EfficientNet
    8. Vision Transformer
    9. Hugging Face Transformers
    10. CleanRL
    11. OpenAI Gym
    12. Farama Gymnasium
    13. MiniGrid
    14. DreamerV3 (danijar/dreamerv3)
    15. TorchRL

    AI recommended 15 alternatives but never named StarsfieldAI/R1-V. 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 StarsfieldAI/R1-V?
    pass
    AI did not name StarsfieldAI/R1-V — 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 StarsfieldAI/R1-V in production, what risks or prerequisites should they evaluate first?
    pass
    AI named StarsfieldAI/R1-V 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 StarsfieldAI/R1-V solve, and who is the primary audience?
    pass
    AI named StarsfieldAI/R1-V explicitly

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

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StarsfieldAI/R1-V — 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