REPOGEO REPORT · LITE
StarsfieldAI/R1-V
Default branch main · commit e35f97e5 · scanned 5/10/2026, 1:17:06 AM
GitHub: 4,054 stars · 285 forks
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
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Clarify project domain in README introduction
Why:
CURRENT# R1-V: Reinforcing Super Generalization Ability in Vision Language Models with Less Than $3
COPY-PASTE FIX# R1-V: Reinforcing Super Generalization Ability in Vision Language Models with Less Than $3 *Note: This project focuses on Vision Language Models (VLMs) and is not related to the Starfield video game.*
- hightopics#2Add relevant topics for categorization
Why:
COPY-PASTE FIXvision-language-models, vlm, reinforcement-learning, rl, visual-reasoning, gui-agent, deep-learning, ai-agent, low-cost-ai
- mediumlicense#3Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) that aligns with the project's goals for contribution and usage.
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.
- OpenCLIP · recommended 1×
- BLIP-2 · recommended 1×
- LLaVA · recommended 1×
- CLIP · recommended 1×
- OWL-ViT · recommended 1×
- CATEGORY QUERYHow to achieve super generalization in vision language models with minimal computational cost?you: not recommendedAI recommended (in order):
- OpenCLIP
- BLIP-2
- LLaVA
- CLIP
- OWL-ViT
- Segment Anything Model (SAM)
AI recommended 6 alternatives but never named StarsfieldAI/R1-V. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat frameworks enable reinforcement learning for vision language models to create intelligent visual agents?you: not recommendedAI recommended (in order):
- Hugging Face Transformers (huggingface/transformers)
- TRL (huggingface/trl)
- RLlib (ray-project/ray)
- Stable Baselines3 (DLR-RM/stable-baselines3)
- DeepMind's Acme (deepmind/acme)
- PyTorch Lightning (Lightning-AI/lightning)
- TorchRL (pytorch/rl)
AI recommended 7 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 completenessfail
Suggestion:
- README presencepass
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?passAI 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?passAI 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?passAI named StarsfieldAI/R1-V 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 StarsfieldAI/R1-V. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/StarsfieldAI/R1-V)<a href="https://repogeo.com/en/r/StarsfieldAI/R1-V"><img src="https://repogeo.com/badge/StarsfieldAI/R1-V.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
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