EP244: Training decentralized AI through private handoffs

Title: Learning to Hand Off: Provably Convergent Workflow Learning under Interface ConstraintsSource: http://arxiv.org/abs/2605.19140v1Summary:This research provides the first finite-sample guarantee for neural Q-learning in decentralized multi-agent settings, a foundational breakthrough for reliable agentic workflow learning. By formalizing handoffs as interface-constrained SMDPs, it enables provably convergent learning in complex LLM pipelines where agents have restricted observability.