Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in ...

Post-Quantum Cryptography: The Case For Agility Over Certainty

​For much of the past decade, post-quantum cryptography (PQC) lived primarily in academic journals and standards committees.
IEEE

Improvement of Real-Scale Ship Model Parameter Identification Algorithm based on Support Vector Machine

Abstract: As offshore operation requirements increase the complexity of maneuvers, high-precision ship models help predict ship movements and improve control accuracy and safety. Identifying unknown ...
Morning Overview on MSN

Google’s new AI compression could cut demand for NAND, pressuring Micron

A new compression technique from Google Research threatens to shrink the memory footprint of large AI models so dramatically ...
GitHub

Python implementation of the TurboQuant and QJL vector quantization algorithms.

turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...
IEEE

A Weighted Distance-Time Algorithm for Large-Scale Task Allocation with Time Window Constraints in Multi-Robot Systems

Abstract: This paper proposes a weighted distance-time algorithm to address the problem of reliably and efficiently allocating large-scale tasks with time window constraints in multi-robot systems.