The field of 6G network intelligence is moving towards a more integrated and autonomous approach, with a focus on native AI capabilities and converged AI-RAN architectures. This direction is driven by the need for more efficient, reliable, and flexible wireless networks that can support a wide range of applications and services. Researchers are exploring new frameworks and architectures that can enable the dynamic coexistence of real-time RAN and computationally intensive AI workloads, as well as the integration of artificial intelligence into radio access networks.

Noteworthy papers in this area include: KP-A, which proposes a unified Network Knowledge Plane for agentic network intelligence, streamlining development and reducing maintenance complexity. Towards AI-Native RAN, which explores the design and standardization principles of AI-Native radio access networks for 6G, with a focus on its critical Day 1 architecture and functionalities. Proactive AI-and-RAN Workload Orchestration, which proposes a Converged AI-and-ORAN Architectural framework enabling the dynamic coexistence of real-time RAN and computationally intensive AI workloads. RIDAS, which proposes a multi-agent framework composed of representation-driven agents and an intention-driven agent to bridge the gap between high-level user intents and low-level parameterized configurations.