This preprint determines the sharp universal exponent of the ordered two-site sweep associated with normalized bijective solutions of the Araoka–Tokihiro functional equation in dimension twelve over the binary field.It proves that every such two-site sweep has period dividing thirty-two and that this bound is attained. The proof combines a general algebraic saturation theorem for three successive scalar translation lifts with exact computer-assisted closure of the remaining finite branches.The algebraic part eliminates one of the three possible dimension-ten configurations without requiring a classification of the corresponding dimension-nine quotients. The other two configurations are resolved by exact binary linear algebra, component-voltage reductions, source-equation certificates, verified transports between presentations, and explicit lift-back checks.The accompanying computation archive contains the source data, finite certificates, reproducibility scripts, independent source-level verification for the largest finite branch, and the low-dimensional verifier suite used by the proof. All decisive finite calculations use exact arithmetic rather than floating-point approximations.The result concerns normalized solutions and the ordered two-site sweep. It does not claim the full many-site period conjecture for the Araoka–Tokihiro cellular automaton.Research methodology and AI assistance: This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology combining persistent research archives, literature and prior-art investigation, iterative proof exploration, and exact verification procedures. Generative AI was used for mathematical exploration, computational reasoning, literature research, proof development, and manuscript preparation.
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Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.