The system looks clean at 10,000 ft: four tiers, each with a defined frequency and responsibility, connected by tidy arrows. Drop to ground level and the first thing you notice is that the tiers are not connected by arrows — they are connected by household WiFi. Titan sits in one room, Panda on a shelf in another room (not on the robot — session 119 corrected a long-standing placement error in the lens narratives), Pi inside the chassis. The "1 Hz strategic plan" reaching Panda from Titan traverses the same 2.4 GHz band as a microwave oven. When WiFi spikes to 100ms — a cliff edge identified by Lens 04 — the clean hierarchy stalls: Panda receives no new plan, Pi receives no new tactical waypoint, and the robot's only active layer is the 10 Hz lidar ESTOP. The architecture diagram shows four tiers collaborating; the physics shows three tiers occasionally collaborating and one tier (reactive ESTOP) running solo. Physical placement was always hidden inside the tier abstraction.

The second leak is semantic. At 30,000 ft the pitch is "navigates to named goals" — rich, spatial, intentional. At ground level the VLM outputs "LEFT MEDIUM": a qualitative direction and a qualitative distance. No coordinates. No confidence score. No map reference. The 10,000 ft diagram shows Tier 1 sending waypoints to Tier 2, but Tier 2's actual output vocabulary has two words for position (LEFT/CENTER/RIGHT) and two for distance (NEAR/FAR/MEDIUM). The semantic map that bridges this gap — Phase 2c, where scene labels attach to SLAM grid cells — does not exist yet. Until it does, "go to the kitchen" means "turn and go toward the thing the VLM recognizes as kitchen-like," which only works if the kitchen is currently in frame.

The third leak is in the kinematic tier — specifically at the hardware boundary between software and motor. The IMU reports heading at 100 Hz and _imu_turn reads it faithfully. But at speed 30, motor momentum delivers 37° of actual rotation when 5° was requested. The Pico RP2040 acts as IMU bridge over USB serial — if it drops to REPL (a crash mode where it silently stops publishing), the kinematic tier goes dark without alerting the reactive or tactical tiers. The system's 4-tier safety model implicitly assumes each tier is healthy; the Pico REPL failure is an abstraction leak where the hardware reality (a microcontroller with an interactive console) bleeds through the software assumption (a reliable 100 Hz heading stream). Lens 01 identified the temporal surplus of 58 Hz as free signal; Lens 02 identifies the fragility of the substrate that produces it.

The deepest leak is the tier-count itself. The "4-tier hierarchy" is a post-hoc rationalization of how components happen to be wired, not a derivation from first principles. The Pi 5 carries a Hailo-8 AI HAT+ with 26 TOPS of NPU throughput that is currently idle for navigation. YOLOv8n runs on it at 430 FPS with <10ms latency and zero WiFi dependency. Activating it dissolves the 4-tier story into a 5-tier hierarchy with a new L1 safety reflex sitting below the current tier-3 lidar ESTOP: on-robot obstacle detection that pre-empts the reactive tier, survives WiFi drops, and gives pixel-precise bounding boxes instead of qualitative "BLOCKED" tokens (detail in Lens 16 on hardware substrate, and Lens 18 on dual-process architectures). The description "Pi is sensor-only, Panda is the perception brain" is not a physical constraint — it is a convention inherited from the WiFi-coupled topology. The future Orin-NX-native robot will collapse L1+L2+L3 onto a single onboard device and the 4-tier/5-tier distinction disappears entirely. Abstraction elevators reveal not just what each altitude shows, but where the floor numbers themselves are arbitrary.