Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks

arXiv cs.AISeptember 21, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2609.21519v1 Announce Type: new Abstract: Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource management, adaptive control, and zero-touch network operation. While current AI-native architectures increasingly embed intelligence across network functions, they provide little guidance on how optimisation knowledge should be systematically generated, transferred, and exploited by AI models. This paper argues that the Learning-to-Optimize (L2O) represents the missing architectural layer between optimisation and AI-native intelligence. Rather than viewing optimisation merely as an online decision eng

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