D-MEM: Dopamine-Gated Agentic Memory via Reward Prediction Error Routing

Researchers introduce D-MEM, a biologically-inspired memory architecture for AI agents that uses dopamine-like reward prediction error routing to dramatically reduce computational costs. The system reduces token consumption by over 80% and eliminates quadratic scaling bottlenecks by selectively processing only high-importance information through cognitive restructuring.