Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...

Official implementation of STAR, a two-stage framework for learning diverse robot skill abstractions with rotation-augmented residual quantization and autoregressive skill composition. [06/2025] ...

Accurate and precise viral titers are critical in cell & gene therapy and vaccine manufacturing, where dosing, safety margins, and product comparability are tightly linked to reliable vector ...

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

Object-Centric Learning (OCL) aggregates image or video feature maps into object-level feature vectors, termed \textit{slots}. It's self-supervision of reconstructing the input from slots struggles ...

Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality.

SAN FRANCISCO--(BUSINESS WIRE)--Elastic (NYSE: ESTC), the Search AI Company, announced new performance and cost-efficiency breakthroughs with two significant enhancements to its vector search. Users ...

A research team led by Associate Prof. Wang Anting from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS) proposed a method for multidimensional ...