📁 LLM

The LLM archive monitors model releases, quantization updates, reasoning capabilities, and real-world deployment implications for local and hybrid AI. We focus on what materially changes selection and operations: context windows, latency, memory footprint, licensing, and evaluation evidence across open and commercial families. This section is designed for teams that need dependable model intelligence, not hype cycles. Pair these updates with the LLM pillar and references to hardware constraints and framework integration.

Microsoft Copilot is a new tool that supports businesses in Italy, enabling them to leverage artificial intelligence in their workflow. With its integration with Microsoft 365 tools, it provides a default solution for employees working on consulting, delivery, management, and software development.

2025-12-23 Fonte

The recently published Loquacious dataset aims to be a replacement for established English automatic speech recognition (ASR) datasets such as LibriSpeech or TED-Lium. The main goal of the Loquacious dataset is to provide properly defined training and test partitions across many acoustic and language domains, with an open license suitable for both academia and industry.

2025-12-23 Fonte

Google Cloud's 2026 AI Agent Trends Report predicts that AI agents will revolutionize the way we work. This article explores five ways in which AI technology will be transformed to change our working lives.

2025-12-23 Fonte

CodeGEMM is a new approach to optimize performance of large models (LLMs) using quantization. This work presents a new GEMM kernel that replaces dequantization with precomputed inner products between centroids and activations stored in a lightweight codebook.

2025-12-23 Fonte

I grandi modelli di linguaggio (LLM) hanno reso possibile l'utilizzo di sistemi multi-agenti (MAS) in cui molti agenti discutono, criticano e coordinano per risolvere compiti complessi. Tuttavia, la maggior parte degli LLM-based MAS adotta grafici pieni o reti sparse, con poca guida strutturale. Questo articolo esplora come le reti di piccolo mondo possano essere utilizzate per stabilizzare i sistemi multi-agenti.

2025-12-23 Fonte