Top-level scene iterator re-generating per-scene 3-angle poses against the
persistent virtual-center reference, RT-style: ascending time order, zero-order
prediction seeding (fit anchored to the center, prediction only warm-starts the
LMA), single pass on the final combo DSI (no refinement pass - it only existed
because disparity arrived after initial orientations offline). Measurement
engine = proven Interscene.adjustPairsLMAInterscene (reference GPU data set
once); phase B will swap it for the lean TD-average x virtual-center path with
GPU argmax+eigen kernels, keeping this iterator + CSV as the oracle.

- new cuas/rt/CuasPoseRT.testPoseSequence(): reference prep (strength>
  curt_pose_str tile selection, setReferenceGPU with center CLT), stored-pose
  seed/truth from center ErsCorrection scenes_poses, per-scene fit with 3-angle
  param_select (XYZ locked), ERS dt from pose finite differences (disable_ers),
  MB off, coast-on-failure; writes -POSE-RT-TEST.csv + fitted-vs-stored summary
- params curt_pose_test (bool) + curt_pose_str (1.0) - 6 plumbing sites
- OpticalFlow curt_en branch: curt_pose_test runs INSTEAD of detection

Build: mvn compile clean. Runtime validation pending (Eclipse/Eyesis run on
sequence 1773135476_186641, truth = re-adjusted stored poses).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>