REPOGEO REPORT · LITE

tulerfeng/Video-R1

Default branch main · commit 59d65c34 · scanned 6/11/2026, 8:03:04 PM

GitHub: 876 stars · 46 forks

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 tulerfeng/Video-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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

multimodal-llm, video-reasoning, reinforcement-learning, mllm, video-ai, deep-learning, generative-ai, large-language-models

highreadme#2

Reposition the core problem statement in the README

Why:

CURRENT

# Video-R1: Reinforcing Video Reasoning in MLLMs

[📖 Paper] [🤗 Video-R1-7B-model] [🤗 Video-R1-train-data] 
[🤖 Video-R1-7B-model]  [🤖 Video-R1-train-data]

## 👀 About Video-R1

Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based RL, we introduce Video-R1 as **the first work to *systematically* explore the R1 paradigm for eliciting video reasoning** within MLLMs.

COPY-PASTE FIX

# Video-R1: Reinforcing Video Reasoning in MLLMs

Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based RL, we introduce Video-R1 as **the first work to *systematically* explore the R1 paradigm for eliciting video reasoning** within MLLMs.

[📖 Paper] [🤗 Video-R1-7B-model] [🤗 Video-R1-train-data] 
[🤖 Video-R1-7B-model]  [🤖 Video-R1-train-data]

## 👀 About Video-R1

mediumlicense#3

Add a LICENSE file to clarify usage terms

Why:

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT, Apache-2.0, or a custom license if applicable) in the repository root to explicitly state the terms of use.

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 tulerfeng/Video-R1

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Video Swin Transformer

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Video Swin Transformer · recommended 1×
Timesformer (TimeSFormer) · recommended 1×
ViViT (Vision Transformer for Video) · recommended 1×
I3D (Inflated 3D ConvNet) · recommended 1×
R(2+1)D (Residual 2+1D ConvNet) · recommended 1×

CATEGORY QUERY
What techniques exist for improving MLLM temporal and spatial reasoning on video data?
you: not recommended
AI recommended (in order):
1. Video Swin Transformer
2. Timesformer (TimeSFormer)
3. ViViT (Vision Transformer for Video)
4. I3D (Inflated 3D ConvNet)
5. R(2+1)D (Residual 2+1D ConvNet)
6. RAFT
7. PWC-Net
8. Temporal Segment Networks (TSN)
9. VideoLSTM
10. Temporal Relation Networks (TRN)
11. YOLOv8
12. DETR
13. ByteTrack
14. DeepSORT
15. Grounding DINO
16. RoIAlign (from Mask R-CNN)
17. Flamingo
18. Video-LLaMA
19. InternVideo
20. VideoMAE
21. CLIP (Contrastive Language-Image Pre-training)
AI recommended 21 alternatives but never named tulerfeng/Video-R1. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking reinforcement learning approaches to elicit advanced reasoning in multimodal models.
you: not recommended
AI recommended (in order):
1. PPO (Proximal Policy Optimization)
2. DPO (Direct Preference Optimization)
3. Constitutional AI (CAI)
4. AlphaGo/AlphaZero-style Search
5. DreamerV3
AI recommended 5 alternatives but never named tulerfeng/Video-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
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 tulerfeng/Video-R1?
pass
AI named tulerfeng/Video-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 tulerfeng/Video-R1 in production, what risks or prerequisites should they evaluate first?
pass
AI named tulerfeng/Video-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 tulerfeng/Video-R1 solve, and who is the primary audience?
pass
AI did not name tulerfeng/Video-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?

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MARKDOWN (README)

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tulerfeng/Video-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