Most of the research focuses on what each component does in isolation: multi-query VLM at 54 Hz, SLAM occupancy grid at 10 Hz, Context Engine conversation memory, SER emotion at the audio pipeline. The Composition Lab question is different: what happens when two of these systems see each other's output? The matrix above now has nine HIGH-rated pairings (two added from the 2026-04-16 session-119 hardware audit). That density is unusual. It signals that the architecture has reached a combinatorial inflection point — adding one new component produces multiple new capabilities simultaneously, because each new component has high affinity with each existing one. This is the signature of a well-chosen stack. Two of those HIGH pairings are crown jewels on orthogonal axes: the spatial-temporal witness (SLAM + Context Engine, the memory axis) and the dual-process nav loop (Hailo-8 L1 reflex + Panda VLM L2 reasoning, the motion axis). The motion-axis crown jewel is experimentally validated — IROS arXiv 2601.21506 reports 66% latency reduction versus always-on VLM and 67.5% navigation success versus 5.83% VLM-only — and both components are already owned: the Hailo-8 AI HAT+ is idle on the Pi 5 (26 TOPS, YOLOv8n @ 430 FPS local, <10 ms, zero WiFi) and the Panda VLM ships Gemma 4 E2B at 54 Hz. No hardware purchase required. The roadmap question is no longer "can we afford dual-process?" but "why haven't we activated the Hailo-8 yet?"

The offline-safe composition, already in production: ArUco + classical CV + lidar sector clearance. Long before the VLM research landed, Annie shipped an ArUco homing system running entirely on the Pi ARM CPU — cv2.aruco.ArucoDetector + cv2.solvePnP with SOLVEPNP_ITERATIVE, 78 µs per call, marker id=23 at the charging station. No GPU. No WiFi. No cloud. When Panda is offline or WiFi has dropped, this composition still homes Annie to the dock. It is the genuine failover composition: a known fiducial target, a closed-form pose solve, and lidar sector clearance for the approach. The matrix flags this as HIGH (SLAM × ArUco) because it is not hypothetical — it is the composition keeping Annie recoverable during every WiFi outage the household has experienced.

The crown jewel combination: SLAM grid + Context Engine. Call it the spatial-temporal witness. SLAM provides WHERE Annie is. Context Engine provides WHAT WAS SAID and WHAT WAS FELT. Neither system was designed with the other in mind — SLAM is a robotics system, Context Engine is a conversation memory system. But their intersection produces a capability that has no precedent in either: every conversation turn is tagged to a room and a timestamp. "Mom sounded worried in the hallway at 08:50, then calmer in the kitchen at 09:14" is no longer an interpretation — it is a retrievable fact, composed from a SLAM pose log and a Context Engine transcript index. The map stops being a navigation artifact. It becomes a household diary, written by sensor fusion and read by language models. This is what "build the map to remember, not navigate" means in operational terms. Navigation is the side effect. Memory is the product.

The minimal 80% combination: Multi-Query VLM + SLAM + scene labels (Phase 2a + 2c, no embeddings). This is the composition that delivers most of the spatial-temporal witness without the Phase 2d embedding infrastructure (SigLIP 2 on Panda, ~800MB VRAM, complex deployment). Scene labels from VLM scene classification (~15 Hz via alternating frames) attached to SLAM grid cells at current pose is enough to support "Annie, what room am I in?" and "Annie, where did you last see the kitchen table?" The topological richness of place embeddings (visual similarity, loop closure confirmation) can be deferred. The 80% value — a queryable spatial map with room labels, tied to conversation memory — is achievable with one code file change (add cycle_count % N dispatch in NavController._run_loop()) and the Phase 1 SLAM groundwork. The embeddings add the remaining 20%: loop closure improvement, visual similarity queries, and "show me where you saw that" from voice. Worth doing eventually; not required for the core insight to become operationally real.

Tried and abandoned: multi-camera surround view (Tesla-style). The research explicitly excludes this — Annie has one camera. BEV feature projection, 8-camera surround, and 3D voxel occupancy all require geometry from multiple viewpoints. The research checked this architecture and discarded it. Has anything changed? Not on the hardware side. But the spirit of the exclusion — "we need geometry from multiple angles" — has a partial workaround: SLAM provides the geometry that surround cameras would otherwise supply. SLAM gives the global map; the single VLM camera provides local semantic context. This is structurally equivalent to "camera gives semantics, lidar gives geometry, radar gives velocity" from the Waymo principles. Annie's architecture is not Tesla-inspired (no surround cameras) but IS Waymo-inspired (complementary modalities, map-as-prior). The abandoned combination was correct to abandon; the working alternative is already in the design.

What would a roboticist from elder care naturally try? A geriatric care practitioner — not a roboticist — would immediately combine SER + Context Engine + Voice Agent and ignore SLAM entirely. Their framing: "I need to know when Mrs. X sounds distressed, what she said just before, and respond gently." They would build the affective loop (SER tags emotion → Context Engine stores emotion with transcript → Voice Agent retrieves it → responds with care) without caring at all about navigation. This is the emotion-first lens on the same data. The composition is HIGH-rated (SER + Context Engine, SER + Voice Agent). And notably, it requires none of the Phase 1 or Phase 2 navigation infrastructure — it is deployable right now on the existing voice + SER + Context Engine stack. The elder-care practitioner would be horrified that the roboticist spent 12 sessions on navigation before wiring up the emotion layer. They are both correct. The matrix reveals that navigation and affective care are parallel development paths that share no prerequisites but share the crown-jewel combination (spatial-temporal witness) as their convergence point.