Exhaustive parameter sweeps are the de facto method for benchmarking RF pipelines, butthey scale poorly with dimensionality[2]. For example, a 10-parameter grid with 10 points eachrequires 1010 evaluations. Active learning promises to achieve comparable confidence using farfewer samples by targeting the most informative points[1]. This paper constructs a syntheticground-truth generator for an RF performance field … Continue reading Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps