Tech Times

Compile Once, Run Offline: New AI Method Matches 32B Models With a 23MB File

Local AI inference at 32B-parameter quality, no cloud API required: University of Waterloo researchers released PAW on July 2 ...

Waterloo's PAW compiles task specs into 23MB LoRA adapters a 600M-parameter model runs entirely offline.

Local AI inference at 32B-parameter quality, no cloud API required: University of Waterloo researchers released PAW on July 2, 2026, a system that compiles any natural-language task spec into a 23MB ...
Techzine Europe

Anthropic is considering developing its own AI chip with Samsung

Anthropic is exploring the development of its own AI chip and is reportedly in talks with Samsung Electronics about it. The move is part of a broader ...

How AI Inference Sends Decision Making To The Edge

The next phase of AI infrastructure will not be defined by a single destination called “the cloud” or “the edge.” ...
Communications of the ACM

Teaching LLMs to Give Better Answers

As a result, researchers are exploring ways to embed better logic into AI. The goal isn’t so much to make LLMs smarter; it’s ...
IEEE

LogiCode: An LLM-Driven Framework for Logical Anomaly Detection

Abstract: This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond the traditional focus on ...