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

- 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>

README.md

@@ -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 |

docs/user-guide.md

@@ -1300,6 +1300,33 @@ and the [benchmark proof](adr/ADR-147-benchmark-proof.md) for full details.
 The Rust crate `wifi-densepose-worldmodel` connects over that Unix socket and injects
 trajectory priors into the pose tracker automatically when the server is running.
 
+**Accumulate training data and fine-tune for your space (improves prediction accuracy):**
+```bash
+# 1. Record WorldGraph snapshots while people move through the space (~1 hour minimum)
+python3 scripts/occworld_retrain.py record \
+    --server http://localhost:8080 \
+    --out-dir /tmp/snapshots/scene_live \
+    --duration 3600
+
+# 2. Fine-tune VQVAE tokenizer on indoor occupancy
+python3 scripts/occworld_retrain.py vqvae \
+    --snapshots /tmp/snapshots/ \
+    --work-dir out/ruview_vqvae
+
+# 3. Fine-tune autoregressive transformer
+python3 scripts/occworld_retrain.py transformer \
+    --snapshots /tmp/snapshots/ \
+    --vqvae-checkpoint out/ruview_vqvae/latest.pth \
+    --work-dir out/ruview_occworld
+
+# 4. Restart the server with your checkpoint
+~/ml-env/bin/python3 scripts/occworld_server.py /tmp/occworld.sock out/ruview_occworld/latest.pth
+```
+
+`scripts/ruview_occ_dataset.py` is the domain adapter used internally by the retraining
+pipeline — it converts WorldGraph JSON snapshots to OccWorld-format tensors with indoor
+class remapping and zero ego-poses. See ADR-147 Phase 3 for details.
+
 ---
 
 ## Training a Model