Abstract: This study proposes a novel distributed online gradient descent algorithm incorporating a time-decaying forgetting-factor (FF) mechanism. The core innovation lies in introducing a ...

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.

Abstract: This paper introduces a distributed finite-time optimization approach for convex optimization with time-varying cost function in multi-agent systems under an undirected and connected ...