📁 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.

Researchers at the University of Science and Technology in China developed a new reinforcement learning framework that helps train large language models (LLMs) for complex agentic tasks beyond well-defined problems like math and coding. The framework, called Agent-R1, is compatible with popular RL algorithms and shows significant improvements on reasoning tasks that require multiple retrieval stages and multi-turn interactions with tools.

2025-11-30 Fonte

Recently, lawyers have faced challenges with AI usage in court due to misleading use. This article explores the reasons and difficulties that these professionals face when dealing with these issues.

2025-11-30 Fonte

This presentation describes the application of machine learning for flood forecasting, with a focus on Google technology and its progress in the field.

2025-11-29 Fonte
📁 LLM AI generated

Introduction to AutoBNN

AutoBNN is an innovative solution for time series prediction, combining the strengths of BNNs and GPs with compositional kernels.

2025-11-29 Fonte

This article describes automatic learning for meteorological forecasting using generatives, a new approach that revolutionizes the weather industry. The SEEDS model developed by Google Research experts achieves similar results to operational forecasts without the need of enormous resources.

2025-11-29 Fonte
📁 LLM AI generated

Shopping Research in ChatGPT

ChatGPT Shopping Research helps you explore, compare and discover products with personalized buyer's guides to simplify purchasing decisions

2025-11-29 Fonte

I LLams continuano a crescere in dimensione, e la ricerca di un modo efficiente per il loro inferenza è essenziale. La sparsity rappresenta una soluzione promettente per questo problema, offrendo multipli speed-up necessari per l'inferenza su dispositivi esterni.

2025-11-27 Fonte