Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Silicon Valley, CA, March 05, 2026 (GLOBE NEWSWIRE) -- Resilinc, the leading provider of agentic AI-driven supply chain risk and compliance intelligence, today announced it has architected its ...

Moody's is sitting on a gold mine of proprietary, trusted data of the sort critical to successful AI adoption by financial services and other regulated industry clients. One positive reality check to ...

Cathie Wood's flagship venture fund has realigned its heaviest bets following a record-breaking merger between Elon Musk's aerospace giant and his artificial intelligence startup. The ARK Venture Fund ...

Databricks said it raised $5 billion in funding and $2 billion in new debt capacity at a $134 billion valuation. The company also said its annualized revenue exceeded $5.4 billion for the January ...

Abstract: High-resolution remote sensing imagery poses significant challenges for semantic segmentation due to pronounced spatial heterogeneity and varied object scales. While recent Mamba-based ...

Databricks’ Mosaic AI Research team has added a new framework, MemAlign, to MLflow, its managed machine learning and generative AI lifecycle development service. MemAlign is designed to help ...

Abstract: This paper describes two experiments in entity resolution. In both experiments, person references were classified as "linked" or "not linked" by two different methods. The first method used ...