Record scheduler placement and bounded three-form timing with unchanged model hashes. CPU + ANE: 149 ANE / 24 CPU operations, 0.929 ms median, 0.973 ms p95. All 120 timed predictions correct. Results are not utilization or power traces.
Add CUA-S1-FORMS FP16 Core ML conversion and verified artifacts
Add the 706,048-parameter CUA-S1-FORMS decision scorer as a portable FP16 Core ML package and compiled FluidAudio bundle, with encoding helper, pinned provenance, licenses, checksums, and conversion reports.
Validation uses the complete pinned 196-row upstream demo, not a new synthetic dataset. PyTorch, Core ML ALL, and Core ML CPU_AND_NE select all 196 labeled options correctly. Maximum probability differences are 0.003099 and 0.002336 respectively, below the preselected 0.005 tolerance. The unmodified Cua evaluator reports zero wrong actions, wrong targets, or unsafe actions on these saved predictions. Seventeen Python regression tests pass.
FluidAudio's Swift runtime independently matches all 196 decisions against the PyTorch probabilities (maximum error 0.002336), including local package loading, shared compiled-model cache loading, UTF-8 encoding, and concurrent/reordered-option checks. The Swift library builds; local XCTest execution is unavailable because this Mac has Command …
