Hugging Face has announced Ettin Reranker, a family of 6 CrossEncoder models based on ModernBERT that improves search (RAG) accuracy while running up to 8 times faster.
Researchers have introduced DeepTS and DeepScribe, two autonomous AI agent frameworks designed to automate time-series data collection and convert complex physics lectures into structured scientific reports.
RAG (Retrieval-Augmented Generation) improves the accuracy of large language models by enabling direct retrieval from trusted external data sources.
NVIDIA and Google Cloud are celebrating the one-year anniversary of their partnership with over 100,000 developers joining their joint community, focusing on deploying RAG applications and multi-agent pipelines.
Hugging Face's new Ettin Reranker model family features 6 variants ranging from 17M to 1B parameters, setting a new benchmark for reranking performance based on ModernBERT.
