Merge nucleic/sleek-ember-seal-uady into dev

ENERGY_AND_RESIDENCY.md

@@ -19,7 +19,8 @@ placement.
 
 ## macOS
 
-- Generate the ML Program with `convert_coreml.py`, then run the gated `eval.py
+- Generate the float ML Program with `convert_coreml.py`, calibrate the W8A8 candidate
+  with `quantize_coreml.py`, then run the gated `eval.py
   --coreml-model ... --coreml-compute-units cpu-and-ne --compare-onnx ...` command from
   the README. Preserve the conversion manifest and frozen report with the model metrics.
 - Run `inspect_coreml.py` with `--compute-units cpu-and-ne`; preserve its full operation
@@ -36,6 +37,13 @@ placement.
   per inference than CPU-only. On AC, a `.all` GPU retry must separately meet its declared
   GPU operation-share floor before it can activate a deep model.
 
+The first float16 candidate is a diagnostic baseline only: it achieved 94.98% scored
+accuracy and 97.97% agreement with the accepted ONNX artifact, so it fails the rollout
+accuracy and parity gates despite 1.51 ms p95 latency. Its compute plan preferred the ANE
+for 150/165 operations with placement information (90.91%) and 59.71% of estimated cost.
+Do not spend energy-measurement time on that rejected package; repeat placement, latency,
+and energy measurements on the first accuracy-qualified W8A8 package.
+
 ## Windows
 
 - Record the Windows ML execution provider and assigned device after AOT compilation.