| 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](docs/adr/ADR-024-contrastive-csi-embedding-model.md)) |

| 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](https://github.com/ruvnet/ruvector)) |

| 🌍 | **Works Everywhere** | Train once, deploy in any room — adversarial domain generalization strips environment bias so models transfer across rooms, buildings, and hardware ([ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md)) |

No training cameras required — the [Self-Learning system (ADR-024)](docs/adr/ADR-024-contrastive-csi-embedding-model.md) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) ensures the model works in any room, not just the one it trained in.

<details>

<summary><a id="cross-environment-generalization-adr-027"></a><strong>🌍 Cross-Environment Generalization (ADR-027 — Project MERIDIAN)</strong> — Train once, deploy in any room without retraining</summary>

WiFi pose models trained in one room lose 40-70% accuracy when moved to another — even in the same building. The model memorizes room-specific multipath patterns instead of learning human motion. MERIDIAN forces the network to forget which room it's in while retaining everything about how people move.

**What it does in plain terms:**

- Models trained in Room A work in Room B, C, D — without any retraining or calibration data

- Handles different WiFi hardware (ESP32, Intel 5300, Atheros) with automatic chipset normalization

- Knows where the WiFi transmitters are positioned and compensates for layout differences

- Generates synthetic "virtual rooms" during training so the model sees thousands of environments

- At deployment, adapts to a new room in seconds using a handful of unlabeled WiFi frames

**Key Components**

| What | How it works | Why it matters |

|------|-------------|----------------|

| **Gradient Reversal Layer** | An adversarial classifier tries to guess which room the signal came from; the main network is trained to fool it | Forces the model to discard room-specific shortcuts |

| **Geometry Encoder (FiLM)** | Transmitter/receiver positions are Fourier-encoded and injected as scale+shift conditioning on every layer | The model knows *where* the hardware is, so it doesn't need to memorize layout |

| **Hardware Normalizer** | Resamples any chipset's CSI to a canonical 56-subcarrier format with standardized amplitude | Intel 5300 and ESP32 data look identical to the model |

| **Virtual Domain Augmentation** | Generates synthetic environments with random room scale, wall reflections, scatterers, and noise profiles | Training sees 1000s of rooms even with data from just 2-3 |

| **Rapid Adaptation (TTT)** | Contrastive test-time training with LoRA weight generation from a few unlabeled frames | Zero-shot deployment — the model self-tunes on arrival |

| **Cross-Domain Evaluator** | Leave-one-out evaluation across all training environments with per-environment PCK/OKS metrics | Proves generalization, not just memorization |

**Architecture**

**Security hardening:**

- Bounded calibration buffer (max 10,000 frames) prevents memory exhaustion

- `adapt()` returns `Result<_, AdaptError>` — no panics on bad input

- Atomic instance counter ensures unique weight initialization across threads

- Division-by-zero guards on all augmentation parameters

See [`docs/adr/ADR-027-cross-environment-domain-generalization.md`](docs/adr/ADR-027-cross-environment-domain-generalization.md) for full architectural details.

</details>

| [Cross-Environment Generalization (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) | Domain-adversarial training, geometry-conditioned inference, hardware normalization, zero-shot deployment | [ADR-027](docs/adr/ADR-027-cross-environment-domain-generalization.md) |

- **Project MERIDIAN (ADR-027)** — Cross-environment domain generalization: gradient reversal, geometry-conditioned FiLM, virtual domain augmentation, contrastive test-time training; zero-shot room transfer

- **10-phase DensePose training pipeline (ADR-023/027)** — Graph transformer, 6-term composite loss, SONA adaptation, RVF packaging, hardware normalization, domain-adversarial training

- **700+ Rust tests** — All passing, zero mocks