This paper introduces a novel deep reinforcement learning framework for safe multi-agent control in highway merging scenarios, integrating partial attention mechanisms into a QMIX architecture. It proposes both spatial and temporal attention to focus on relevant neighboring vehicles and their historical states, combined with a comprehensive reward signal to balance global traffic objectives and individual agent interests. The approach demonstrates significant improvements in safety, driving speed, and overall reward compared to baseline models in SUMO simulations.

This paper proposes an agentic AI framework for intent-driven optimization in cell-free Open Radio Access Networks (O-RAN), where LLM-based agents collaborate to translate operator intents into network optimizations. The framework demonstrates significant reductions in active O-RUs for energy saving and memory usage through parameter-efficient fine-tuning.