The dependency telescope reveals a system that is far more fragile at its upstream joints than its engineering confidence suggests. The four-tier hierarchical fusion architecture — Titan at Tier 1, Panda VLM at Tier 2, Pi lidar at Tier 3, IMU at Tier 4 — reads as robust modularity. But each tier is tethered to an upstream it does not control. The most consequential of these is not the obvious WiFi dependency: it is llama-server's inability to expose intermediate multimodal embeddings. This single API gap in an open-source inference server blocks Phase 2d (embedding extraction + place memory) entirely, and forces the deployment of a separate SigLIP 2 model that consumes 800 MB of Panda's already-constrained 8 GB VRAM. A limitation in one upstream layer manufactured a hardware budget problem in another.

The WiFi dependency is the system's hidden single point of failure — not because it is unknown, but because it has no engineering mitigation. Every other dependency has a documented workaround or fallback: if Gemma 4 E2B is retired, swap to a different GGUF model; if slam_toolbox stalls, restart the Docker container; if the IMU drops to REPL, soft-reboot the Pico. But if household WiFi degrades, the Pi-to-Panda camera link drops from 54 Hz to something below 10 Hz, and there is no fallback — the system runs degraded silently. Lens 04 identified this as the WiFi cliff edge at 100ms latency. What the Dependency Telescope adds is the cascade: degraded VLM throughput degrades scene classification, which degrades semantic map annotation quality, which degrades Phase 2c room labeling accuracy. A single uncontrolled RF environment poisons three downstream phases. The Session 119 hardware audit surfaced a downstream-dependency mitigation hiding in plain sight: the Pi 5's Hailo-8 AI HAT+ is already on-robot and idle. Activating it as a local L1 safety layer (YOLOv8n at 430 FPS, zero WiFi) rewrites the cascade. "WiFi degrades → all three Phase 2 phases degrade" becomes "WiFi degrades → semantic features degrade, safety stays local." The dependency doesn't disappear — it gets demoted from safety-critical to semantic-only, which is exactly where an uncontrolled RF medium belongs.

The Phase 1 SLAM prerequisite chain deserves special attention because it is the upstream that gates the most downstream value. Phases 2c (semantic map annotation), 2d (embedding extraction and place memory), and 2e (AnyLoc visual loop closure) are all marked "requires Phase 1 SLAM deployed." This means three of the five Phase 2 phases — the three that deliver the most architectural novelty — are in a single-file queue behind one deployment. If Phase 1 SLAM suffers a persistent failure (Zenoh session crash, lidar dropout, IMU brownout), the downstream timeline does not slip by one phase, it slips by three simultaneously. The research acknowledges this in its probability table: Phase 2c is 65%, Phase 2d is 55%, Phase 2e is 50%. Those probabilities are not independent — they are conditionally dependent on the same upstream SLAM health.

The downstream surprises are equally instructive. The research frames the semantic map as a navigation primitive — rooms labeled on a grid. But the voice agent downstream consumer converts that primitive into a qualitatively different capability: spatial memory answerable by voice. Annie can tell you where the charger is, when she last visited the kitchen, or whether the living room is currently occupied — without any additional training, purely because scene labels are attached to SLAM poses. The Context Engine similarly receives a capability it was not designed for: spatial facts in its entity index. Neither downstream consumer is mentioned in the research roadmap. The most valuable accidental enablement is the one most likely to create an integration mismatch when it arrives.