When a worker thread completes a task, it doesn't return a sprawling transcript of every failed attempt; it returns a compressed summary of the successful tool calls and conclusions.

Exam 1 will cover lectures 1-14 (i.e. up to and including Snooping Based Multiprocessor Design). Exam 2 will cover lectures 15-27 (i.e. Prefetching through Parallelism in Database Management Systems).

Generative AI (GenAI) is driving a surge in computing demand, pushing data center competition beyond server performance toward infrastructure efficiency and deployment density, with power architecture ...

High-resolution scans of a pre-dynastic granite vase reveal micrometer-level precision that researchers say is nearly impossible to achieve with primitive hand tools. A mathematician and cryptographer ...

Enterprises that want tokenizer-free multilingual models are increasingly turning to byte-level language models to reduce brittleness in noisy or low-resource text. To tap into that niche — and make ...

Abstract: The emergence of quantum computing threatens classical cryptographic systems, necessitating efficient architectural designs for post-quantum algorithms. This paper presents a novel ...

The data model has "metadata" JSON column on a thread level. The data layer's update_thread() method is called from different places i the backend, sometimes including session-level metadata (e.g.

Low-power computer chip startup Efficient Computer Co. today announced the launch of its new flagship Electron E1 processor, dramatically reducing the energy requirements of general-purpose computing ...

Sparse large language models (LLMs) based on the Mixture of Experts (MoE) framework have gained traction for their ability to scale efficiently by activating only a subset of parameters per token.