🤗 Novita now supports Ling-3.0-flash-VL on @huggingface. 🎁 Free for 14 days. • 124B total parameters · 5.5B active parameters per token • Native image and video understanding • Built for multimodal reasoning and agentic workflows

Ling-3.0-flash-VL is live on DeepInfra — day 0 with @AntLingAGI. https://deepinfra.com/inclusionAI/Ling-3.0-flash-VL

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World Labs co-founders Fei-Fei Li, Justin Johnson, Ben Mildenhall, and a16z's Martin Casado on Atlas, a world model for spatial intelligence: LLMs are built on next token prediction. Video models are built on next frame prediction. Atlas is built on new view prediction, and it's the first model to unify pixel generation and pixel reconstruction, two problems computer vision has kept in separate tracks for half a century. The practical result is a 50 to 100x reduction in what it takes to digitally capture a 3D representation of a space. Previously, you needed 100 to 300 photos of a single room. Atlas can work from just three. In this conversation, they get into the slow motion shot from The Matrix that took hundreds of cameras and now takes three iPhones, the overnight Slack message that made them bet the company in five seconds, why robotics is bottlenecked on data rather than chips, and the case that new view prediction is AI-complete. 00:00 Intro 01:50 The Matrix slow motion scene now takes three iPhones 02:48 Why new view prediction is the primitive 07:10 Unifying generation and reconstruction 11:15 Gaussian splats became the bottleneck 14:17 Dense capture used to mean 300 photos 17:30 Why reconstruction needs generation to fill the gaps 18:44 The LLM lesson image models missed 23:39 The video that made them go all in 28:04 3D design is 95% revisions 30:50 The problem in robotics is data, not chips 32:48 Why a robot policy can't be trained like an image model 34:44 When the simulator becomes the planner 36:45 Frozen time required footage full of movement 40:57 Why new view prediction is AI-complete 42:43 Nature gave animals eyes but not trees YouTube: https://www.youtube.com/watch?v=qn1QDDBnTA0 @drfeifei @jcjohnss @BenMildenhall @theworldlabs @martin_casado

Introducing Atlas: The world's first multimodal world model that generates image and video frames with pixel-perfect camera control and reconstructs them in 3D. Model the world, move the camera, and simulate space & time.

🚀 What if video generators could build on representations that already understand the visual world? We are excited to introduce V-RAE: Rethinking Video Latent Spaces for Generation. Recent progress in image generation has begun to move beyond conventional VAE latents, exploring both direct pixel-space and representation-based approaches. Video generation, however, still depends heavily on latent compression, as the scale and redundancy of spatiotemporal data make direct modeling prohibitively expensive. However, most video VAEs are optimized for pixel reconstruction, and a latent space that reconstructs well is not necessarily easy to generate. V-RAE takes a different approach: it directly uses representations from frozen vision foundation models as the generative latent space, rather than as auxiliary supervision. We study DINOv3, SigLIP2, EUPE, and V-JEPA 2.1. A lightweight temporal attention pooling module compresses their dense features by 4×, followed by a spatiotemporal Transformer decoder. Under matched generation backbones, latent budgets, and training settings, V-RAE achieves: 🏆 2.13 rFVD on Kinetics-600 🎬 117.86 gFVD on UCF101 and 19.16 gFVD on Kinetics-600 ⚡ Up to 6× faster convergence than VAE-based latent spaces 🧠 90.92% semantic probing accuracy on UCF101 🌍 Better future prediction on Cityscapes, reducing gFVD from 144.47 to 111.36 Our experiments also reveal a broader finding: Good Reconstruction ≠ Good Generation. During generation, predicted latents inevitably deviate from real encoding trajectories. If the latent space is not sufficiently smooth, small errors can be amplified into visible artifacts. We therefore introduce tFVD to evaluate temporal smoothness and robustness to latent prediction errors. It correlates much more strongly with downstream generation quality, reaching 0.919 on Kinetics-600. The takeaway: A latent space is not merely where videos are compressed—it determines what the generator must learn. When semantics and temporal structure are already organized in the representation, generation becomes easier to learn. Representation first. Generation follows. Many thanks to my mentors, @ScottNLP and @SQWu_Tori, for their continuous guidance and support. I am also deeply grateful to @sainingxie for his valuable guidance and invaluable feedback, which greatly helped shape V-RAE. 🙏 Hi @_akhaliq, we would truly appreciate your help in sharing V-RAE with the broader AI research community. Thank you! 🙏 📄 Paper: https://arxiv.org/abs/2608.13556 💻 Code: https://github.com/V-RAE/V-RAE 🤗 Models: https://huggingface.co/Guomh0707/V-RAE-Models 🌐 Project: https://v-rae.github.io #VideoGeneration #GenerativeAI #ComputerVision #WorldModels #RepresentationLearning #RAE