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OpenAI 1 year ago 11
Researchers improved continuous-time consistency models to match the sample quality of leading diffusion models while requiring only two sampling steps instead of many. The models achieved this through simplified architecture, enhanced training stability, and improved scaling methods. This reduces the computational cost of image generation, making the approach more practical for deployment.
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TLDRocket
4 October 2026
Google’s biggest AI story today wasn’t a bigger model—it was a more enforceable way to train on messy, private data. In work that combines federated learning with trusted execution environments, Google Research introduced a TEE-based system for Gboard that delivers externally verifiable central differential privacy guarantees. In plain terms, the privacy math isn’t just claimed; it’s produced in a hardware-backed environment that others can audit. The result is a stronger story for what “privacy-preserving” should mean when you’re predicting the next word on a phone.
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