Bartosz Belter | Searcharxiv

arXiv2026

Assured AI-Native Network Control Loops: State of the Art, Research Challenges and the Missing Runtime Assurance Layer

The evolution towards autonomous and AI-native telecommunication networks is transforming network control from predefined automation towards distributed and intelligent decision-making. Advances in closed-loop automation, O-RAN, Network Digital Twins, AI-driven orchestration, and autonomous agents enable multiple specialized control functions to operate concurrently across network domains and timescales. This introduces a system-level assurance challenge: individually acceptable decisions may interact through shared resources and network state, while changes in their operational context may invalidate assumptions under which they were evaluated. This paper presents a state-of-the-art review of AI-native network control, focusing on the composition and runtime assurance of autonomous control loops. It examines closed-loop and zero-touch automation, intelligent controllers, Network Digital Twins, AI-driven orchestration, autonomous agents, trustworthy AI, and runtime assurance. The analysis shows that these directions provide important foundations for autonomous operation but lack a unified mechanism for assuring heterogeneous control loops whose decisions depend on shared and dynamically changing network state. To address this gap, the paper identifies dependency-aware runtime assurance as a research direction for assured composition of AI-native network control loops. The proposed perspective associates decisions with assumptions and dependencies on which their validity relies, monitors changes that may invalidate accepted decisions, and supports runtime resolution of concurrent control interactions. A telecom use case and an initial architecture and formal model illustrate the concept and identify open challenges in dependency representation, runtime validation, conflict resolution, latency-aware assurance, and experimental evaluation.

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arXiv2025

SLICES, a scientific instrument for the networking community

A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking components and architectures have been assisted, among other methods, by experimentally-driven research and in particular by the deployment of test platforms, usually named as testbeds. However, often experimentally-driven networking research used scattered methodologies, based on ad-hoc, small-sized testbeds, producing hardly repeatable results. We believe that computer networks needs to adopt a more structured methodology, supported by appropriate instruments, to produce credible experimental results supporting radical and incremental innovations. This paper reports lessons learned from the design and operation of test platforms for the scientific community dealing with digital infrastructures. We introduce the SLICES initiative as the outcome of several years of evolution of the concept of a networking test platform transformed into a scientific instrument. We address the challenges, requirements and opportunities that our community is facing to manage the full research-life cycle necessary to support a scientific methodology.

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