feat(worldmodel): ADR-147 — OccWorld world model integration, wifi-densepose-worldmodel v0.3.0 (#856)

* feat(worldmodel): ADR-147 — OccWorld integration, wifi-densepose-worldmodel v0.3.0 (#854)

- New crate `wifi-densepose-worldmodel` v0.3.0: async Unix-socket bridge
  to OccWorld Python inference server; `OccWorldBridge`, `OccupancyGrid3D`,
  `TrajectoryPrior`, `worldgraph_to_occupancy` encoder (14/14 tests pass)
- `scripts/occworld_server.py`: long-lived Python inference server for
  OccWorld TransVQVAE (72.4M params); applies API-bug patches; dummy mode
  for CI testing; graceful SIGTERM shutdown
- `pose_tracker.rs`: `trajectory_prior` soft-blend injection (80/20
  Kalman/prior) on torso keypoint; `set_trajectory_prior()` public method
- CI: added `Run ADR-147 worldmodel tests` step
- ADR-147: accepted — OccWorld primary (209 ms, 3.37 GB VRAM, RTX 5080);
  Cosmos deferred to ADR-148 (32.54 GB VRAM exceeds hardware)
- Benchmark proof: 208.7 ms P50, 3.37 GB peak VRAM, 12.1 GB headroom

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

* chore: update ruvector.db state

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

* chore: ruvector.db sync

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

* fix(cli): add missing min_frames field to CalibrateArgs test helper

E0063 in calibrate.rs:448 — CalibrateArgs gained min_frames in ADR-135
but the default_args() test helper was not updated. min_frames=0 means
'use tier default', matching the existing runtime behaviour.

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

docs/user-guide.md

@@ -34,7 +34,8 @@ WiFi DensePose turns commodity WiFi signals into real-time human pose estimation
     - [Recording Training Data](#recording-training-data)
     - [Training the Model](#training-the-model)
     - [Using the Trained Model](#using-the-trained-model)
-13. [Training a Model](#training-a-model)
+13. [World Model Prediction (OccWorld)](#world-model-prediction-occworld)
+14. [Training a Model](#training-a-model)
     - [CRV Signal-Line Protocol](#crv-signal-line-protocol)
 14. [RVF Model Containers](#rvf-model-containers)
 14. [Hardware Setup](#hardware-setup)
@@ -1281,6 +1282,26 @@ Once trained, the adaptive model runs automatically:
 
 ---
 
+## World Model Prediction (OccWorld)
+
+RuView integrates [OccWorld](https://github.com/wzzheng/OccWorld) (ECCV 2024) to predict
+future 3D occupancy from WiFi CSI — extending the Kalman tracker's 5-frame horizon to
+15 predicted frames (~7 s). See [ADR-147](adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)
+and the [benchmark proof](adr/ADR-147-benchmark-proof.md) for full details.
+
+**Hardware requirement:** NVIDIA GPU with ≥4 GB VRAM (validated: RTX 5080 at 209 ms / 3.4 GB).
+
+**Start the inference server:**
+```bash
+# Requires ml-env with PyTorch 2.7+ and mmcv/mmdet3d installed (see ADR-147 §3)
+~/ml-env/bin/python3 scripts/occworld_server.py /tmp/occworld.sock
+```
+
+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.
+
+---
+
 ## Training a Model
 
 The training pipeline is implemented in pure Rust (7,832 lines, zero external ML dependencies).