feat(mobile): refine spatial target UI

docs/adr/ADR-342-cognitum-inspired-mobile-instrument-ui.md

@@ -337,6 +337,13 @@ consume only the existing fail-closed displayable track list. Do not add
 `expo-gl`, React Three Fiber, a WebView, a raw point-cloud schema, or a new sensor
 adapter in this UI-only decision.
 
+Render explicit target-lock reticles, a relay-to-hidden depth scale, confidence,
+and a persistent `RECONSTRUCTION / NOT RAW SCAN` boundary in the cloud surface.
+The reticle geometry reuses bounded marker objects and updates their positions in
+place so frame changes do not create a new WebGL context or allocate new marker
+meshes. These cues improve operator target acquisition without promoting the
+schematic cloud into physical sensor evidence.
+
 ## Alternatives with quantified tradeoffs
 
 Alternatives are scored from 1, poor, to 5, strong. Weighted score is out of

docs/screenshots/consumer-nlos-mobile-ui/README.md

@@ -4,7 +4,8 @@ These images are deterministic review captures from the production Expo web
 export at a 390 by 844 viewport. The Playwright flow navigates the real mobile
 application, exercises the synthetic replay control, verifies provenance and
 watermark requirements, opens the deterministic Three.js LiDAR point cloud,
-and writes the PNG files in this directory.
+verifies the reconstruction boundary, target lock, and confidence HUD, and
+writes the PNG files in this directory.
 
 The captures contain only the disconnected state, governed setup copy, and the
 built in synthetic fixture. The point cloud is generated locally from gated