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Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe are all specified by hand. We demonstrate that this design layer can be surrendered to an autonomous agent in its entirety. We adopt the autoresearch protocol, in which an AI coding agent edits a training script, runs a fixed-budget experiment, and retains or discards the change according to a single immutable metric. We grant the agent authority over the architecture family, the input representation, the output parameterization, the loss funct

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