Research is typically evaluated by the answers it provides. The more productive evaluation is the questions it makes possible to ask for the first time. Before Annie proved 58 Hz monocular VLM navigation on a $200 robot, five of the questions in this analysis were not merely unanswered — they were not yet coherent. "Can one VLM frame serve 4 tasks simultaneously?" presupposes a pipeline fast enough that frame allocation is a meaningful design variable. "Can a semantic map transfer between homes?" presupposes a semantic map at all. "Why does the robot need to understand language?" presupposes a working non-language path worth comparing against. None of these could be seriously asked before the 58 Hz result existed. The research created the conditions for its own successors.

The most structurally important of the five branches is Branch 5: the outsider question "why does the robot need to understand language at all?" It is structurally important because insiders cannot ask it. The team chose a Vision-Language Model — language is in the name. Language is assumed. The outsider, arriving from animal cognition or control theory, immediately sees the mismatch: the navigation problem is geometric (where am I, where is the goal, what is between me and the goal) and the robot is solving it by translating geometry into natural language and then translating language back into geometry. The text layer is a relay station between two signal types that don't need an interpreter. An ant colony navigating complex terrain does not pass its pheromone gradients through a language model. Lens 08 makes the same observation from neuroscience: rat hippocampal place cells encode spatial identity directly as activation patterns, not as verbal descriptions of the place. The text-language layer is the architecturally interesting thing to remove — and that question only becomes askable once the research proves the vision encoder already has everything needed for navigation without it.

Three branches converge on the same answer from independent starting points: bypass the text-language layer. Branch 1 arrives there through task-parallelism (what if embeddings instead of text for each frame?), Branch 3 arrives through map transfer (what if SLAM cells stored embeddings instead of text labels?), and Branch 4 arrives through cross-field comparison to cognitive science and animal navigation (what if place recognition used raw ViT features rather than text descriptions?). The text2nav result (RSS 2025) — 74% navigation success with frozen SigLIP embeddings alone — is the empirical anchor for all three. These three lines of inquiry converge on one architectural change: remove the text-decoding step from the Tier 2 (tactical, 58 Hz) perception loop while retaining text at Tier 1 (strategic, 1-2 Hz) where language is actually needed to interpret human goals. The convergence is not coincidence. It reflects the structure of the research: the research built a system that works, and the bottleneck that now stands between "working" and "excellent" is the translation overhead the system inherited from its model class rather than from its task.

Branch 2 — the almost-answered question about EMA temporal consistency — is worth examining precisely because the research stops just short of its most important implication. The research proposes EMA alpha=0.3 producing 86 ms of consistency memory, and notes this filters single-frame hallucinations. What it never asks: does EMA on VLM outputs predict SLAM loop closure events? If Annie's scene variance spikes every time SLAM independently detects a revisited location, the VLM is doing place recognition through the text layer without being asked to. This would mean the 150M-parameter vision encoder already detects "I've been here before" as a byproduct of its scene stability signal, and the text decoding pipeline is the barrier preventing that signal from being used directly. The almost-answered question points at the convergence point from yet another direction. The research got within one analysis step of discovering that EMA variance is already a text-mediated place recognition signal.

Branch 3 — the 10x multiplier question — is the one with the clearest business consequence. If Annie's semantic map transfers between homes (because it stores concept embeddings rather than room coordinates), the map becomes a product distinct from the robot. A new user's Annie could bootstrap orientation in an unfamiliar environment from a pre-trained concept graph rather than requiring full blind exploration. "Kitchen-ness," "bathroom-ness," and "living-room-ness" are not home-specific — they are culturally stable semantic clusters. The fraction of the concept graph that transfers (hypothesis: 60-70%) minus the fraction that is home-specific (hypothesis: 30-40%) determines the commercial value of semantic map sharing. That calculation could not be set up before this research existed. It now can.

Branch 6 — the dual-process horizon opened by session 119 — is the first branch that was not visible at the time of the primary research and became visible only because a targeted hardware-inventory pass ran in parallel with a literature sweep. Two findings emerged at once: the IROS 2601.21506 result (System 1 / System 2 dual-process, 66% latency reduction, 67.5% vs 5.83% success on indoor robot nav) and an idle 26 TOPS Hailo-8 AI HAT+ already paid for and mounted on Annie's Pi 5 — running zero inferences for navigation, capable of YOLOv8n at 430 FPS in under 10 ms with no WiFi dependency. The pair is load-bearing: IROS supplies the architectural pattern and Hailo supplies the substrate that makes the pattern free to adopt. Four new questions became askable in a single session: the tuning question (at what query rate does System 2 gating win?), the layer-ratio question (what are the optimal relative Hz for L1/L2/L3/L4 once dual-process lands?), the Hailo capability question (can it run NanoOWL-lite open-vocabulary, or only closed-class YOLO?), and the meta-question (what other idle compute is in the house that nobody has audited?). The meta-question is the one that propagates beyond this research. The Hailo-8 was not a design success — nobody designed Annie to use it; it came with the Pi 5 AI kit. It was a process success: a targeted audit found a previously-invisible resource. The explicit question "what else is idle?" is the durable output of session 119, and it points at Beast, Orin NX 16 GB, and unaudited household compute (phones, laptops, TV SoCs) as the next places to look.