arXiv:2605.23809v1 Announce Type: cross Abstract: The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) offer strong reasoning and code-generation capabilities but are unsuited for the fast, deterministic inference required in real-time RAN control. We present a proof-of-concept Dual-Brain architecture that combines both strengths: an LLM-based orchestr
The increasing sophistication of LLMs and the architectural flexibility of O-RAN are converging, making their integration a natural next step for autonomous network management.
This development indicates a significant leap in network automation and efficiency, leveraging advanced AI to manage complex radio access networks with greater adaptability and reduced human intervention.
The conventional manual and slow process of developing and deploying AI applications in O-RAN transforms into an automated, 'Dual-Brain' orchestration, significantly accelerating innovation and operational agility.
- · Telecommunication operators
- · AI developers
- · O-RAN vendors
- · Cloud infrastructure providers
- · Traditional network management software vendors
- · Companies relying on manual network optimization
Increased efficiency and reduced operational costs for telecom providers due to automated network management.
Accelerated deployment of new services and features in wireless networks, fostering innovation and competition.
Potential for new business models and services that leverage hyper-optimized, autonomous wireless infrastructure.
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Read at arXiv cs.LG