Update README.md

README.md

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-## Introduction
+# Qwen3-VL-235B-A22B-Thinking
 
-The goal of **Qwen3-VL** is not just to “see” images or videos — but to truly understand the world, interpret events, and take action. To achieve this, we’ve systematically upgraded key capabilities, pushing visual models from simple “perception” toward deeper “cognition,” and from basic “recognition” to advanced “reasoning and execution.”
-![](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/qwen3vl_arc.jpg) 
 
-**Key Highlights:**
+Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
 
-- **Visual Agent Capabilities**: Qwen3-VL can operate computer and mobile interfaces — recognize GUI elements, understand button functions, call tools, and complete tasks. It achieves top global performance on benchmarks like OS World, and using tools significantly improves its performance on fine-grained perception tasks.
+This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
 
-- **Superior Pure-text Performance**: Qwen3-VL employs early-stage joint pretraining of text and visual modalities, continuously strengthening its language capabilities. It ultimately achieves pure-text task performance comparable to Qwen3-235B-A22B-2507 — the flagship pure-text model — making it a truly “text-grounded, multimodal powerhouse” for the next generation of vision-language models.
+Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
 
-- **Greatly Improved Visual Coding**: It can now generate code from images or videos — for example, turning a design mockup into Draw.io, HTML, CSS, or JavaScript code — making “what you see is what you get” visual programming a reality.
 
-- **Much Better Spatial Understanding**: It can judge object positions, viewpoint changes, and occlusion relationships. It supports 3D grounding, laying the foundation for complex spatial reasoning and embodied AI applications.
+#### Key Enhancements:
 
-- **Long Context & Long Video Understanding**: All models natively support 256K tokens of context, expandable up to 1 million tokens. This means you can input hundreds of pages of technical documents, entire textbooks, or even two-hour meeting or lecture videos — and the model will remember everything and retrieve details accurately, down to the exact second in videos.
+* **Visual Agent**: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.
 
-- **Stronger Multimodal Reasoning (Thinking Version)**: The Thinking model is specially optimized for STEM and math reasoning. When facing complex subject questions, it can notice fine details, break down problems step by step, analyze cause and effect, and give logical, evidence-based answers. It achieves SOTA performance on authoritative benchmarks like MathVision, MMMU, and MathVista.
+* **Visual Coding Boost**: Generates Draw.io/HTML/CSS/JS from images/videos.
 
-- **Upgraded Visual Perception & Recognition**: By improving the quality and diversity of pre-training data, the model can now recognize a much wider range of objects — from celebrities, anime characters, products, and landmarks, to animals and plants — covering both everyday life and professional “recognize anything” needs.
+* **Advanced Spatial Perception**: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.
+
+* **Long Context & Video Understanding**: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.
+
+* **Enhanced Multimodal Reasoning**: Excels in STEM/Math—causal analysis and logical, evidence-based answers.
+
+* **Upgraded Visual Recognition**: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.
+
+* **Expanded OCR**: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.
+
+* **Text Understanding on par with pure LLMs**: Seamless text–vision fusion for lossless, unified comprehension.
+
+
+#### Model Architecture Updates:
+
+<p align="center">
+    <img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/qwen3vl_arc.jpg" width="80%"/>
+<p>
+
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+1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.
+
+2. **DeepStack**: Fuses multi‑level ViT features to capture fine‑grained details and sharpen image–text alignment.
+
+3. **Text–Timestamp Alignment:** Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.
 
-- **Better OCR Across More Languages & Complex Scenes**: OCR now supports 32 languages (up from 10), covering more countries and regions. It performs more reliably under challenging real-world conditions like poor lighting, blur, or tilted text. Recognition accuracy for rare characters, ancient scripts, and technical terms has also improved significantly. Its ability to understand long documents and reconstruct fine structures is further enhanced.
 
 This is the weight repository for Qwen3-VL-235B-A22B-Thinking.