A calibrated light-sensor model helps a robot locate a known white circle with fewer reported measurements. The task uses fixed lighting, distance and prior shape assumptions. Supplied uncertainty values are missing, and native equation, count and date discrepancies remain unresolved. These results do not establish general object recognition or UAP performance.
Complete supplied18-page preprint read; final version and raw gapsNested Entropy Sampling searches for informative experiments in synthetic two-dimensional landscapes. Small sample counts can become trapped at local peaks, while higher counts reduce compression. The robot example is a simulation, and reported computation savings count entropy evaluations. Finite successes do not establish global convergence or physical measurement validation.
Complete supplied8-page preprint read; final proceedings and implementation gapsA curated public selection. Source review describes what was read; it does not establish that a claim is true. Our research approach