Researchers at North Carolina State University have developed an AI-enhanced electrochemical sensing method to quickly and accurately quantify viral vectors used in gene therapies. The approach uses ...
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] ...
LumaCyte today announced that its analytical approach has been included in the newly published International Organization for Standardization (ISO) global standard for gene delivery systems, ISO 16921 ...
Google says a new compression algorithm, called TurboQuant, can compress and search massive AI data sets with near-zero indexing time, potentially removing one of the biggest speed limits in modern ...
TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...
Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...
Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...
AI has a growing memory problem. Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression ...
The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size ...
Abstract: Vector quantization (VQ) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effectively address this ...
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