feat: Add 12 ADRs for RuVector RVF integration and proof-of-reality

Comprehensive architecture decision records for integrating ruvnet/ruvector into wifi-densepose, covering: - ADR-002: Master integration strategy (phased rollout, new crate design) - ADR-003: RVF cognitive containers for CSI data persistence - ADR-004: HNSW vector search replacing fixed-threshold detection - ADR-005: SONA self-learning with LoRA + EWC++ for online adaptation - ADR-006: GNN-enhanced pattern recognition with temporal modeling - ADR-007: Post-quantum cryptography (ML-DSA-65 hybrid signatures) - ADR-008: Raft consensus for multi-AP distributed coordination - ADR-009: RVF WASM runtime for edge/browser/IoT deployment - ADR-010: Witness chains for tamper-evident audit trails - ADR-011: Mock elimination and proof-of-reality (fixes np.random.rand placeholders, ships CSI capture + SHA-256 verified pipeline) - ADR-012: ESP32 CSI sensor mesh ($54 starter kit specification) - ADR-013: Feature-level sensing on commodity gear (zero-cost RSSI path) ADR-011 directly addresses the credibility gap by cataloging every mock/placeholder in the Python codebase and specifying concrete fixes. https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
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+# ADR-005: SONA Self-Learning for Pose Estimation
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Static Model Problem
+
+The WiFi-DensePose modality translation network (`ModalityTranslationNetwork` in Python, `ModalityTranslator` in Rust) converts CSI features into visual-like feature maps that feed the DensePose head for body segmentation and UV coordinate estimation. These models are trained offline and deployed with frozen weights.
+
+**Critical limitations of static models**:
+
+1. **Environment drift**: CSI characteristics change when furniture moves, new objects are introduced, or building occupancy changes. A model trained in Lab A degrades in Lab B without retraining.
+
+2. **Hardware variance**: Different WiFi chipsets (Intel AX200 vs Broadcom BCM4375 vs Qualcomm WCN6855) produce subtly different CSI patterns. Static models overfit to training hardware.
+
+3. **Temporal drift**: Even in the same environment, CSI patterns shift with temperature, humidity, and electromagnetic interference changes throughout the day.
+
+4. **Population bias**: Models trained on one demographic may underperform on body types, heights, or movement patterns not represented in training data.
+
+Current mitigation: manual retraining with new data, which requires:
+- Collecting labeled data in the new environment
+- GPU-intensive training (hours to days)
+- Model export/deployment cycle
+- Downtime during switchover
+
+### SONA Opportunity
+
+RuVector's Self-Optimizing Neural Architecture (SONA) provides <1ms online adaptation through:
+
+- **LoRA (Low-Rank Adaptation)**: Instead of updating all weights (millions of parameters), LoRA injects small trainable rank decomposition matrices into frozen model layers. For a weight matrix W ∈ R^(d×k), LoRA learns A ∈ R^(d×r) and B ∈ R^(r×k) where r << min(d,k), so the adapted weight is W + AB.
+
+- **EWC++ (Elastic Weight Consolidation)**: Prevents catastrophic forgetting by penalizing changes to parameters important for previously learned tasks. Each parameter has a Fisher information-weighted importance score.
+
+- **Online gradient accumulation**: Small batches of live data (as few as 1-10 samples) contribute to adaptation without full backward passes.
+
+## Decision
+
+We will integrate SONA as the online learning engine for both the modality translation network and the DensePose head, enabling continuous environment-specific adaptation without offline retraining.
+
+### Adaptation Architecture
+
+```
+┌──────────────────────────────────────────────────────────────────────┐
+│                    SONA Adaptation Pipeline                          │
+├──────────────────────────────────────────────────────────────────────┤
+│                                                                      │
+│  Frozen Base Model                    LoRA Adaptation Matrices       │
+│  ┌─────────────────┐                  ┌──────────────────────┐      │
+│  │ Conv2d(64,128)  │ ◀── W_frozen ──▶ │ A(64,r) × B(r,128) │      │
+│  │ Conv2d(128,256) │ ◀── W_frozen ──▶ │ A(128,r) × B(r,256)│      │
+│  │ Conv2d(256,512) │ ◀── W_frozen ──▶ │ A(256,r) × B(r,512)│      │
+│  │ ConvT(512,256)  │ ◀── W_frozen ──▶ │ A(512,r) × B(r,256)│      │
+│  │ ...             │                  │ ...                  │      │
+│  └─────────────────┘                  └──────────────────────┘      │
+│         │                                      │                     │
+│         ▼                                      ▼                     │
+│  ┌─────────────────────────────────────────────────────────┐        │
+│  │            Effective Weight = W_frozen + α(AB)           │        │
+│  │            α = scaling factor (0.0 → 1.0 over time)     │        │
+│  └─────────────────────────────────────────────────────────┘        │
+│                              │                                       │
+│                              ▼                                       │
+│  ┌─────────────────────────────────────────────────────────┐        │
+│  │                    EWC++ Regularizer                      │        │
+│  │  L_total = L_task + λ Σ F_i (θ_i - θ*_i)²              │        │
+│  │                                                          │        │
+│  │  F_i = Fisher information (parameter importance)         │        │
+│  │  θ*_i = optimal parameters from previous tasks           │        │
+│  │  λ = regularization strength (10-100)                    │        │
+│  └─────────────────────────────────────────────────────────┘        │
+└──────────────────────────────────────────────────────────────────────┘
+```
+
+### LoRA Configuration per Layer
+
+```rust
+/// SONA LoRA configuration for WiFi-DensePose
+pub struct SonaConfig {
+    /// LoRA rank (r): dimensionality of adaptation matrices
+    /// r=4 for encoder layers (less variation needed)
+    /// r=8 for decoder layers (more expression needed)
+    /// r=16 for final output layers (maximum adaptability)
+    lora_ranks: HashMap<String, usize>,
+
+    /// Scaling factor alpha: controls adaptation strength
+    /// Starts at 0.0 (pure frozen model), increases to target
+    alpha: f64,  // Target: 0.3
+
+    /// Alpha warmup steps before reaching target
+    alpha_warmup_steps: usize,  // 100
+
+    /// EWC++ regularization strength
+    ewc_lambda: f64,  // 50.0
+
+    /// Fisher information estimation samples
+    fisher_samples: usize,  // 200
+
+    /// Online learning rate (much smaller than offline training)
+    online_lr: f64,  // 1e-5
+
+    /// Gradient accumulation steps before applying update
+    accumulation_steps: usize,  // 10
+
+    /// Maximum adaptation delta (safety bound)
+    max_delta_norm: f64,  // 0.1
+}
+```
+
+**Parameter budget**:
+
+| Layer | Original Params | LoRA Rank | LoRA Params | Overhead |
+|-------|----------------|-----------|-------------|----------|
+| Encoder Conv1 (64→128) | 73,728 | 4 | 768 | 1.0% |
+| Encoder Conv2 (128→256) | 294,912 | 4 | 1,536 | 0.5% |
+| Encoder Conv3 (256→512) | 1,179,648 | 4 | 3,072 | 0.3% |
+| Decoder ConvT1 (512→256) | 1,179,648 | 8 | 6,144 | 0.5% |
+| Decoder ConvT2 (256→128) | 294,912 | 8 | 3,072 | 1.0% |
+| Output Conv (128→24) | 27,648 | 16 | 2,432 | 8.8% |
+| **Total** | **3,050,496** | - | **17,024** | **0.56%** |
+
+SONA adapts **0.56% of parameters** while achieving 70-90% of the accuracy improvement of full fine-tuning.
+
+### Adaptation Trigger Conditions
+
+```rust
+/// When to trigger SONA adaptation
+pub enum AdaptationTrigger {
+    /// Detection confidence drops below threshold over N samples
+    ConfidenceDrop {
+        threshold: f64,     // 0.6
+        window_size: usize, // 50
+    },
+
+    /// CSI statistics drift beyond baseline (KL divergence)
+    DistributionDrift {
+        kl_threshold: f64,  // 0.5
+        reference_window: usize, // 1000
+    },
+
+    /// New environment detected (no close HNSW matches)
+    NewEnvironment {
+        min_distance: f64,  // 0.8 (far from all known fingerprints)
+    },
+
+    /// Periodic adaptation (maintenance)
+    Periodic {
+        interval_samples: usize, // 10000
+    },
+
+    /// Manual trigger via API
+    Manual,
+}
+```
+
+### Adaptation Feedback Sources
+
+Since WiFi-DensePose lacks camera ground truth in deployment, adaptation uses **self-supervised signals**:
+
+1. **Temporal consistency**: Pose estimates should change smoothly between frames. Jerky transitions indicate prediction error.
+   ```
+   L_temporal = ||pose(t) - pose(t-1)||² when Δt < 100ms
+   ```
+
+2. **Physical plausibility**: Body part positions must satisfy skeletal constraints (limb lengths, joint angles).
+   ```
+   L_skeleton = Σ max(0, |limb_length - expected_length| - tolerance)
+   ```
+
+3. **Multi-view agreement** (multi-AP): Different APs observing the same person should produce consistent poses.
+   ```
+   L_multiview = ||pose_AP1 - transform(pose_AP2)||²
+   ```
+
+4. **Detection stability**: Confidence should be high when the environment is stable.
+   ```
+   L_stability = -log(confidence) when variance(CSI_window) < threshold
+   ```
+
+### Safety Mechanisms
+
+```rust
+/// Safety bounds prevent adaptation from degrading the model
+pub struct AdaptationSafety {
+    /// Maximum parameter change per update step
+    max_step_norm: f64,
+
+    /// Rollback if validation loss increases by this factor
+    rollback_threshold: f64,  // 1.5 (50% worse = rollback)
+
+    /// Keep N checkpoints for rollback
+    checkpoint_count: usize,  // 5
+
+    /// Disable adaptation after N consecutive rollbacks
+    max_consecutive_rollbacks: usize,  // 3
+
+    /// Minimum samples between adaptations
+    cooldown_samples: usize,  // 100
+}
+```
+
+### Persistence via RVF
+
+Adaptation state is stored in the Model Container (ADR-003):
+- LoRA matrices A and B serialized to VEC segment
+- Fisher information matrix serialized alongside
+- Each adaptation creates a witness chain entry (ADR-010)
+- COW branching allows reverting to any previous adaptation state
+
+```
+model.rvf.model
+  ├── main (frozen base weights)
+  ├── branch/adapted-office-2024-01 (LoRA deltas)
+  ├── branch/adapted-warehouse (LoRA deltas)
+  └── branch/adapted-outdoor-disaster (LoRA deltas)
+```
+
+## Consequences
+
+### Positive
+- **Zero-downtime adaptation**: Model improves continuously during operation
+- **Tiny overhead**: 17K parameters (0.56%) vs 3M full model; <1ms per adaptation step
+- **No forgetting**: EWC++ preserves performance on previously-seen environments
+- **Portable adaptations**: LoRA deltas are ~70 KB, easily shared between devices
+- **Safe rollback**: Checkpoint system prevents runaway degradation
+- **Self-supervised**: No labeled data needed during deployment
+
+### Negative
+- **Bounded expressiveness**: LoRA rank limits the degree of adaptation; extreme environment changes may require offline retraining
+- **Feedback noise**: Self-supervised signals are weaker than ground-truth labels; adaptation is slower and less precise
+- **Compute on device**: Even small gradient computations require tensor math on the inference device
+- **Complexity**: Debugging adapted models is harder than static models
+- **Hyperparameter sensitivity**: EWC lambda, LoRA rank, learning rate require tuning
+
+### Validation Plan
+
+1. **Offline validation**: Train base model on Environment A, test SONA adaptation to Environment B with known ground truth. Measure pose estimation MPJPE (Mean Per-Joint Position Error) improvement.
+2. **A/B deployment**: Run static model and SONA-adapted model in parallel on same CSI stream. Compare detection rates and pose consistency.
+3. **Stress test**: Rapidly change environments (simulated) and verify EWC++ prevents catastrophic forgetting.
+4. **Edge latency**: Benchmark adaptation step on target hardware (Raspberry Pi 4, Jetson Nano, browser WASM).
+
+## References
+
+- [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
+- [Elastic Weight Consolidation (EWC)](https://arxiv.org/abs/1612.00796)
+- [Continual Learning with SONA](https://github.com/ruvnet/ruvector)
+- [Self-Supervised WiFi Sensing](https://arxiv.org/abs/2203.11928)
+- ADR-002: RuVector RVF Integration Strategy
+- ADR-003: RVF Cognitive Containers for CSI Data