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Pulse · bloomberg.com

bloomberg.com · Nov 13

Post-Jensen US Chip Curbs Threaten Southeast Asia's AI Boom

Southeast Asia risks facing tighter US regulations on the export and use of high-powered chips, threatening to stall the region's AI-fueled economic boom.

Pulse · hindustantimes.com

hindustantimes.com · Nov 13

Nvidia's $10 billion bet on Anthropic IPO, and the chip guarantees behind the AI industry's borrowing

Jensen Huang's exit as Nvidia's chief executive, a week before Anthropic is due to begin trading in what could be the biggest stock market listing on record, has put a spotlight on the chipmaker's $10 billion pledge to the artificial intelligence (AI ...

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Top picks this weekarXiv

arXiv

Nov 12

Back to Pixels: Lifting Latent Video Diffusion into Pixel Space

Frontier video generation models work in a VAE latent space that compresses video in both space and time. This compression made training tractable, but now limits high-resolution, controllable, and interactive generation -- doubling resolution quadruples token count, conditions apply to groups of frames rather than individual ones, and each response must wait for the rest of its multi-frame block. We present PixVid, a simple yet effective method that lifts a pretrained latent video model to directly generate every frame in pixel space. PixVid keeps the diffusion transformer and replaces the VAE decoder with a convolutional head that renders its outputs into pixels. We find that predicting clean video rather than velocity, together with a larger noise shift, is key to quality. Finetuned from Wan2.1-14B, PixVid is competitive with the latent baseline within $\sim 1.3$M training clips. Working framewise in pixels, it generates at $2\times$ higher resolution with the same per-frame token count, keeps depth-conditioned video editing aligned under fast motion, and responds to each action at the next frame. Project page: https://chenshuo20.github.io/PixVid/