TD-average the 16 sensors BEFORE correlation (multiply averages, not average
products), single conj-multiply vs the persistent virtual-center TD. Zero new
CUDA - assembled from proven pieces:
- CuasPoseRT.leanMeasure(): interCorrTD(sensor_mask=0) = tasks(pose)+offsets+
  convert_direct only -> getCltData/CuasTD.consolidateSensorsTD/setCltData
  (the validated CPU bridge, future clt_average_sensors kernel) ->
  setSensorMaskInter(1)+execCorr2D_inter_TD (single conj-multiply) ->
  TDCorrTile.getFromGpu + convertTDtoPD (JNA-validated CuasMotion path,
  FZ-normalize + PD) -> Correlation2d.getMaxXYCmEig (peak+eigen, the GPU
  argmax kernel oracle). Correlation stages are geometry-blind - projection/
  distortion is baked into the average-camera tasks (per Andrey).
- CuasPoseRT.leanFitScene(): same IntersceneLma solver + exit rules as the
  oracle engine; fills lma_rms/coord_motion_rslt so CSV/-POSE-RT-HYPER are
  unchanged (A2-03 = direct oracle).
- curt_pose_lean checkbox 'Pose test lean correlation (B)'.
v1 differences from oracle (documented): NO motion-blur compensation (compare
vs NOMB baseline: 0.287/0.282/0.106 mrad), no FPN peak masking (input is
FPN-subtracted by A2 conditioning), no moving-object filter, min_confidence=0.

mvn compile clean.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>