feat: ADR-033 CRV signal-line integration + ruvector-crv 6-stage pipeline

Implement full CRV (Coordinate Remote Viewing) signal-line protocol
mapping to WiFi CSI sensing via ruvector-crv:

- Stage I: CsiGestaltClassifier (6 gestalt types from amplitude/phase)
- Stage II: CsiSensoryEncoder (texture/color/temperature/sound/luminosity/dimension)
- Stage III: Mesh topology encoding (AP nodes/links → GNN graph)
- Stage IV: Coherence gate → AOL detection (signal vs noise separation)
- Stage V: Pose interrogation via differentiable search
- Stage VI: Person partitioning via MinCut clustering
- Cross-session convergence for cross-room identity

New files:
- crv/mod.rs: 1,430 lines, 43 tests
- crv_bench.rs: 8 criterion benchmarks (gestalt, sensory, pipeline, convergence)
- ADR-033: 740-line architecture decision with 30+ acceptance criteria
- patches/ruvector-crv: Fix ruvector-gnn 2.0.5 API mismatch

Dependencies: ruvector-crv 0.1.1, ruvector-gnn 2.0.5

Co-Authored-By: claude-flow <ruv@ruv.net>

rust-port/wifi-densepose-rs/patches/ruvector-crv/README.md

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+# ruvector-crv
+
+CRV (Coordinate Remote Viewing) protocol integration for ruvector.
+
+Maps the 6-stage CRV signal line methodology to ruvector's subsystems:
+
+| CRV Stage | Data Type | ruvector Component |
+|-----------|-----------|-------------------|
+| Stage I (Ideograms) | Gestalt primitives | Poincaré ball hyperbolic embeddings |
+| Stage II (Sensory) | Textures, colors, temps | Multi-head attention vectors |
+| Stage III (Dimensional) | Spatial sketches | GNN graph topology |
+| Stage IV (Emotional) | AOL, intangibles | SNN temporal encoding |
+| Stage V (Interrogation) | Signal line probing | Differentiable search |
+| Stage VI (3D Model) | Composite model | MinCut partitioning |
+
+## Quick Start
+
+```rust
+use ruvector_crv::{CrvConfig, CrvSessionManager, GestaltType, StageIData};
+
+// Create session manager with default config (384 dimensions)
+let config = CrvConfig::default();
+let mut manager = CrvSessionManager::new(config);
+
+// Create a session for a target coordinate
+manager.create_session("session-001".to_string(), "1234-5678".to_string()).unwrap();
+
+// Add Stage I ideogram data
+let stage_i = StageIData {
+    stroke: vec![(0.0, 0.0), (1.0, 0.5), (2.0, 1.0), (3.0, 0.5)],
+    spontaneous_descriptor: "angular rising".to_string(),
+    classification: GestaltType::Manmade,
+    confidence: 0.85,
+};
+
+let embedding = manager.add_stage_i("session-001", &stage_i).unwrap();
+assert_eq!(embedding.len(), 384);
+```
+
+## Architecture
+
+The Poincaré ball embedding for Stage I gestalts encodes the hierarchical
+gestalt taxonomy (root → manmade/natural/movement/energy/water/land) with
+exponentially less distortion than Euclidean space.
+
+For AOL (Analytical Overlay) separation, the spiking neural network temporal
+encoding models signal-vs-noise discrimination: high-frequency spike bursts
+correlate with AOL contamination, while sustained low-frequency patterns
+indicate clean signal line data.
+
+MinCut partitioning in Stage VI identifies natural cluster boundaries in the
+accumulated session graph, separating distinct target aspects.
+
+## Cross-Session Convergence
+
+Multiple sessions targeting the same coordinate can be analyzed for
+convergence — agreement between independent viewers strengthens the
+signal validity:
+
+```rust
+// After adding data to multiple sessions for "1234-5678"...
+let convergence = manager.find_convergence("1234-5678", 0.75).unwrap();
+// convergence.scores contains similarity values for converging entries
+```
+
+## License
+
+MIT OR Apache-2.0