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arXiv AI By Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari

Evolving Inspectable O-RAN Slicing xApps with LLMs

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The paper presents a method for creating Open RAN (O‑RAN) slicing xApps that are both adaptive and fully inspectable. By using a large language model (LLM) to evolve compact Python programs, the authors generate slicing controllers whose decision logic remains readable and editable after optimization. In experiments on the NSF POWDER 5G testbed, the evolved controllers improved best‑effort throughput by 44.5% and reduced SLA misses from 79.9% to 2.2%, while evolutionary search outperformed independent prompting in trace‑driven simulations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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