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docs: add Phase 3+5 scripts to user guide and README world model row

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- User guide: full retrain workflow (record → vqvae → transformer → serve)
  with checkpoint path usage
- README: note fine-tune capability in world model capability row

Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-05-29 19:50:21 -04:00
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README.md
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@@ -62,7 +62,7 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
> | 🌍 **World model prediction** | OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080 ([ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)) | 15 frames × 200×200×16 vox |
> | 🌍 **World model prediction** | OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with `occworld_retrain.py` ([ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)) | 15 frames × 200×200×16 vox |
> | 🧱 **Through-wall sensing** | Fresnel-zone geometry + multipath modeling | Up to ~5 m, signal-dependent |
> | 🧠 **Edge intelligence** | **105-cog catalog** ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) live from `app-registry.json` — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain | $140 total BOM |
> | 🎯 **Camera-free pre-training** | Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face | 84 s/epoch retrain on M4 Pro |