REPOGEO REPORT · LITE

OpenBMB/DeepThinkVLA

Default branch main · commit 0b03dd35 · scanned 5/27/2026, 7:27:11 PM

GitHub: 524 stars · 47 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 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 OpenBMB/DeepThinkVLA, 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

Strengthen README's initial positioning for robotics and deliberative reasoning

Why:

CURRENT

<p align="center">
  Enhancing Reasoning Capability of Vision-Language-Action Models
</p>

COPY-PASTE FIX

<p align="center">
  Enhancing Reasoning Capability of Vision-Language-Action Models
</p>
<p align="center"><b>A novel framework for embodied AI, focusing on hierarchical planning and explicit deliberation in complex robotic tasks.</b></p>

mediumtopics#2

Expand GitHub topics with more specific keywords

Why:

CURRENT

reasoning-models, rl, robotics, vla

COPY-PASTE FIX

reasoning-models, rl, robotics, vla, embodied-ai, multi-modal-llm, decision-making, action-generation, deliberative-planning, hierarchical-planning

lowreadme#3

Add a 'Why DeepThinkVLA?' section to highlight differentiators

Why:

COPY-PASTE FIX

## ✨ Why DeepThinkVLA? (Our Differentiators)

DeepThinkVLA stands apart from general Vision-Language Models (VLMs) and Large Language Models (LLMs) by introducing a hierarchical planning mechanism that integrates high-level language understanding and reasoning with low-level action generation specifically for embodied AI. This allows it to break down complex tasks and execute them more robustly and flexibly, addressing the limitations of existing visual language agents in comprehensive tool learning and multi-step reasoning for real-world robotic tasks.

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 OpenBMB/DeepThinkVLA

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

GPT-4V (Vision)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

GPT-4V (Vision) · recommended 1×
Google Gemini (Pro/Ultra) · recommended 1×
llava-vl/llava · recommended 1×
DeepMind's AlphaCode 2 · recommended 1×
pyreason/pyreason · recommended 1×

CATEGORY QUERY
How to improve reasoning and decision-making in vision-language-action models for robotics?
you: not recommended
AI recommended (in order):
1. GPT-4V (Vision)
2. Google Gemini (Pro/Ultra)
3. LLaVA (Large Language and Vision Assistant) (llava-vl/llava)
4. DeepMind's AlphaCode 2
5. PyReason (pyreason/pyreason)
6. RLlib (Ray) (ray-project/ray)
7. Stable Baselines3 (DLR-RM/stable-baselines3)
8. RDFox
9. Protege
10. PandaPlanner (from Google Robotics) (google-research/panda-robot)
11. ROS MoveIt! Task Constructor (MTC) (ros-planning/moveit_task_constructor)
AI recommended 11 alternatives but never named OpenBMB/DeepThinkVLA. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks exist for training and evaluating advanced vision-language-action models with complex reasoning?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Accelerate
3. PyTorch Lightning
4. JAX
5. Flax
6. TensorFlow
7. Keras
8. OpenAI Gym
9. Farama Foundation Gymnasium
10. RLlib
11. Ray
AI recommended 11 alternatives but never named OpenBMB/DeepThinkVLA. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
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 OpenBMB/DeepThinkVLA?
pass
AI named OpenBMB/DeepThinkVLA explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts OpenBMB/DeepThinkVLA in production, what risks or prerequisites should they evaluate first?
pass
AI named OpenBMB/DeepThinkVLA 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 OpenBMB/DeepThinkVLA solve, and who is the primary audience?
pass
AI named OpenBMB/DeepThinkVLA explicitly
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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HTML

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OpenBMB/DeepThinkVLA — 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