惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Recent Commits to openclaw:main
Recent Commits to openclaw:main
博客园 - 叶小钗
Stack Overflow Blog
Stack Overflow Blog
S
SegmentFault 最新的问题
D
DataBreaches.Net
S
Securelist
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
T
Threatpost
C
Cyber Attacks, Cyber Crime and Cyber Security
The Hacker News
The Hacker News
Jina AI
Jina AI
T
Threat Research - Cisco Blogs
GbyAI
GbyAI
Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
Engineering at Meta
Engineering at Meta
T
The Exploit Database - CXSecurity.com
A
Arctic Wolf
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
C
Cisco Blogs
PCI Perspectives
PCI Perspectives
Project Zero
Project Zero
G
Google Developers Blog
宝玉的分享
宝玉的分享
H
Heimdal Security Blog
美团技术团队
Schneier on Security
Schneier on Security
C
CERT Recently Published Vulnerability Notes
Martin Fowler
Martin Fowler
博客园 - 司徒正美
博客园 - 三生石上(FineUI控件)
Help Net Security
Help Net Security
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Google DeepMind News
Google DeepMind News
C
Check Point Blog
Hacker News: Ask HN
Hacker News: Ask HN
L
LINUX DO - 最新话题
O
OpenAI News
Hacker News - Newest:
Hacker News - Newest: "LLM"
N
Netflix TechBlog - Medium
S
Security Affairs
小众软件
小众软件
MongoDB | Blog
MongoDB | Blog
Blog — PlanetScale
Blog — PlanetScale
V
V2EX - 技术
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
F
Fortinet All Blogs
G
GRAHAM CLULEY
云风的 BLOG
云风的 BLOG
S
Secure Thoughts

Hugging Face - Blog

Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs ALTK‑Evolve: On‑the‑Job Learning for AI Agents Safetensors is Joining the PyTorch Foundation Holo3: Breaking the Computer Use Frontier Any Custom Frontend with Gradio's Backend A New Framework for Evaluating Voice Agents (EVA) Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations One-Shot Any Web App with Gradio's gr.HTML CUGA on Hugging Face: Democratizing Configurable AI Agents New in llama.cpp: Model Management Building Deep Research: How we Achieved State of the Art OVHcloud on Hugging Face Inference Providers 🔥 20x Faster TRL Fine-tuning with RapidFire AI Building for an Open Future - our new partnership with Google Cloud Aligning to What? Rethinking Agent Generalization in MiniMax M2 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac Sentence Transformers is joining Hugging Face! Unlock the power of images with AI Sheets Supercharge your OCR Pipelines with Open Models Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face Get your VLM running in 3 simple steps on Intel CPUs Nemotron-Personas-India: Synthesized Data for Sovereign AI Introducing RTEB: A New Standard for Retrieval Evaluation Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models VibeGame: Exploring Vibe Coding Games Nemotron-Personas-Japan: ソブリン AI のための合成データセット Swift Transformers Reaches 1.0 – and Looks to the Future Smol2Operator: Post-Training GUI Agents for Computer Use SyGra: The One-Stop Framework for Building Data for LLMs and SLMs Gaia2 and ARE: Empowering the community to study agents Scaleway on Hugging Face Inference Providers 🔥 Democratizing AI Safety with RiskRubric.ai Public AI on Hugging Face Inference Providers 🔥 `LeRobotDataset:v3.0`: Bringing large-scale datasets to `lerobot` Visible Watermarking with Gradio Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason! Tricks from OpenAI gpt-oss YOU 🫵 can use with transformers Fine-tune Any LLM from the Hugging Face Hub with Together AI Jupyter Agents: training LLMs to reason with notebooks mmBERT: ModernBERT goes Multilingual Welcome EmbeddingGemma, Google's new efficient embedding model SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence Make your ZeroGPU Spaces go brrr with ahead-of-time compilation NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset Generate Images with Claude and Hugging Face From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels MCP for Research: How to Connect AI to Research Tools Kimina-Prover-RL Arm & ExecuTorch 0.7: Bringing Generative AI to the masses Neural Super Sampling is here! TextQuests: How Good are LLMs at Text-Based Video Games? 🇵🇭 FilBench - Can LLMs Understand and Generate Filipino? Introducing AI Sheets: a tool to work with datasets using open AI models! Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training Vision Language Model Alignment in TRL ⚡️ Welcome GPT OSS, the new open-source model family from OpenAI! Measuring Open-Source Llama Nemotron Models on DeepResearch Bench 📚 3LM: A Benchmark for Arabic LLMs in STEM and Code Implementing MCP Servers in Python: An AI Shopping Assistant with Gradio Introducing Trackio: A Lightweight Experiment Tracking Library from Hugging Face Say hello to `hf`: a faster, friendlier Hugging Face CLI ✨ Parquet Content-Defined Chunking TimeScope: How Long Can Your Video Large Multimodal Model Go? Fast LoRA inference for Flux with Diffusers and PEFT Accelerate a World of LLMs on Hugging Face with NVIDIA NIM Arc Virtual Cell Challenge: A Primer Consilium: When Multiple LLMs Collaborate Back to The Future: Evaluating AI Agents on Predicting Future Events Five Big Improvements to Gradio MCP Servers Ettin Suite: SoTA Paired Encoders and Decoders Migrating the Hub from Git LFS to Xet Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models Asynchronous Robot Inference: Decoupling Action Prediction and Execution ScreenEnv: Deploy your full stack Desktop Agent Building the Hugging Face MCP Server Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders Creating custom kernels for the AMD MI300 Upskill your LLMs With Gradio MCP Servers SmolLM3: smol, multilingual, long-context reasoner Three Mighty Alerts Supporting Hugging Face’s Production Infrastructure Efficient MultiModal Data Pipeline Announcing NeurIPS 2025 E2LM Competition: Early Training Evaluation of Language Models Training and Finetuning Sparse Embedding Models with Sentence Transformers Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub Gemma 3n fully available in the open-source ecosystem! Transformers backend integration in SGLang (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware Groq on Hugging Face Inference Providers 🔥 How Long Prompts Block Other Requests - Optimizing LLM Performance Learn the Hugging Face Kernel Hub in 5 Minutes Convert Transformers to ONNX with Hugging Face Optimum Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration Director of Machine Learning Insights [Part 3: Finance Edition] The Annotated Diffusion Model Deep Q-Learning with Space Invaders Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers Introducing Pull Requests and Discussions 🥳 Efficient Table Pre-training without Real Data: An Introduction to TAPEX An Introduction to Q-Learning Part 2/2 How Sempre Health is leveraging the Expert Acceleration Program to accelerate their ML roadmap
NVIDIA Cosmos Reason 2 Brings Advanced Reasoning To Physical AI
Tsung-Yi Lin, Debraj Sinha · 2026-01-06 · via Hugging Face - Blog

Back to Articles

Tsung-Yi Lin's avatar

Debraj Sinha's avatar

NVIDIA today released Cosmos Reason 2, the latest advancement in open, reasoning vision language models for physical AI. Cosmos Reason 2 surpasses its previous version in accuracy and tops the Physical AI Bench and Physical Reasoning leaderboards as the #1 open model for visual understanding.

NVIDIA Cosmos Reason 2: Reasoning Vision Language Model for Physical AI

Since their introduction, vision-language models have rapidly improved at tasks like object and pattern recognition in images. But they still struggle with tasks humans find natural, like planning several steps ahead, dealing with uncertainty or adapting to new situations. Cosmos Reason is designed to close this gap by giving robots and AI agents stronger common sense and reasoning to solve complex problems step by step.

Cosmos Reason 2 is a state-of-the-art, open reasoning vision-language model (VLM) that enables robots and AI agents to see, understand, plan, and act in the physical world like humans. It uses common sense, physics, and prior knowledge to recognize how objects move across space and time to handle complex tasks, adapt to new situations, and figure out how to solve problems step by step.

✨ Key Highlights

  • Improved spatio-temporal understanding and timestamp precision.

  • Optimized performance with flexible deployment options from edge to cloud with 2B and 8B parameters model sizes.

  • Support for expanded set of spatial understanding and visual perception capabilities — 2D/3D point localization, bounding box coordinates, trajectory data, and OCR support.

  • Improved long-context understanding with 256K input tokens, up from 16K with Cosmos Reason 1.

  • Adaptable to multiple use cases with easy-to-use Cosmos Cookbook recipes.

🤖 Popular Use Cases

  • Video analytics AI agents — These agents can extract valuable insights from massive volumes of video data to optimize processes. Cosmos Reason 2 builds on the capabilities of Cosmos Reason 1 and now provides OCR support, as well as 2D/3D point localization and a set of mark understanding.

    Example of how Cosmos Reason can understand text embedded within a video to determine the condition of the road during a rainstorm.

    Developers can jumpstart development of video analytics AI agents by using the NVIDIA blueprint for video search and summarization (VSS) with Cosmos Reason as the VLM.

    Salesforce is transforming workplace safety and compliance by analyzing video footage captured by Cobalt robots with Agentforce and VSS blueprint with Cosmos Reason as the VLM.

  • Data annotation and critique — Enable developers to automate high-quality annotation and critique of massive, diverse training datasets. Cosmos Reason provides time stamps and detailed descriptions for real or synthetically generated training videos.

    Data annotation and critique example
    Example of a sample prompt to generate detailed, time-stamped captions for a race car video.

    Uber is exploring Cosmos Reason 2 to deliver accurate, searchable video captions for autonomous vehicle (AV) training data, enabling efficient identification of critical driving scenarios. This co-authored Reason 2 for AV Video Captioning and VQA recipe demonstrates how to fine-tune and evaluate Cosmos Reason 2-8B on annotated AV videos. Across multiple evaluation metrics, measurable improvements were achieved: BLEU scores improved 10.6% (0.113 → 0.125), MCQ-based VQA gained 0.67 percentage points (80.18% → 80.85%), and LingoQA increased 13.8% (63.2% → 77.0%). These gains demonstrate effective domain adaptation for AV applications.

  • Robot planning and reasoning — Act as the brain for deliberate, methodical decision-making in a robot vision language action (VLA) model. Cosmos Reason 2 now provides trajectory coordinates in addition to determining next steps.

    Example of the prompt and JSON output from Cosmos Reason 2 to provide the steps and trajectory the robot gripper needs to take to move the painter’s tape into the basket.

    Encord provides native support for Cosmos Reason 2 in its Data Agent library and AI data platform, enabling developers to leverage Cosmos Reason 2 as a VLA for robotics and other physical AI use cases.

Companies like Hitachi, Milestone and VAST Data are using Cosmos Reason to advance robotics, autonomous driving, and video analytics AI agents for traffic and workplace safety.

Try Cosmos Reason 2 on build.nvidia.com and experience the latest features with sample prompts for generating bounding boxes and robot trajectories. Upload your own videos and images for further analysis.

Download Cosmos Reason 2 models (2B and 8B) on Hugging Face or use Cosmos Reason 2 in the cloud. The model will be available soon on Amazon Web Services, Google Cloud and Microsoft Azure. To get started, check out Cosmos Reason 2 documentation and the Cosmos Cookbook.

Other Models From The Cosmos Family:

🔮 Cosmos Predict 2.5

Cosmos Predict is a generative AI model that predicts future states of the physical world as video, based on text, image, or video inputs.

  • Physical AI Bench leader for quality, accuracy and overall consistency.
  • Up to 30 seconds of physically and temporally consistent clip per generation.
  • Supports multiple framerates and resolution.
  • Pre-trained on 200 million clips.
  • Available as 2B and 14B pre-trained models and various 2B post-trained models for multiview, action conditioning and autonomous vehicle training.

Check out model card>>

🔁 Cosmos Transfer 2.5

Cosmos Transfer is our lightest multicontrol model built for video to world style transfer.

  • Scale a single simulation or spatial video across various environments and lighting conditions.
  • Improved prompt adherence and physics alignment.
  • Use with NVIDIA Isaac Sim™ or NVIDIA Omniverse NuRec for simulation to real transformation.

Check out model card>>

🤖 NVIDIA GR00T N1.6

NVIDIA GR00T N1.6 is an open reasoning vision language action (VLA) model, purpose-built for humanoid robots, that unlocks full body control and uses NVIDIA Cosmos Reason for better reasoning and contextual understanding.

Resources

▶️ Watch a demo of Cosmos → https://youtu.be/iWs-2TD5Dcc

🧑🏻‍🍳 Read the Cosmos Cookbook → https://nvda.ws/4qevli8

📚 Explore Models & Datasets → https://github.com/nvidia-cosmos

⬇️ Try Cosmos Models in our Hosted Catalog → https://nvda.ws/3Yg0Dcx

💻 Join the Cosmos Community → https://discord.gg/u23rXTHSC9

🗳️ Contribute to the Cosmos Cookbook → https://nvda.ws/4aQcBkk