diff --git a/scripts/pose-inference-proxy.README.md b/scripts/pose-inference-proxy.README.md
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+# pose-inference-proxy
+
+Sidecar that plumbs `model.safetensors` inference into the sensing server's
+WebSocket broadcast so the dashboard renders real model output.
+
+## Why
+
+The sensing server accepts `--model <path>` but the runtime broadcast loop
+emits `pose_keypoints: None` and keypoint confidences of `0.0` regardless of
+whether a model is loaded. The loaded weights are read once at startup for
+the `Layer A ready` log line and never fed into the broadcast path. This
+script fills the gap as a sidecar so the wiring can be observed and the
+dashboard can render real inference output without recompiling the Rust
+crate.
+
+## Run
+
+```bash
+pip install numpy safetensors websockets
+huggingface-cli download ruvnet/wifi-densepose-pretrained \
+  --local-dir models/wifi-densepose-pretrained
+
+# 1. Start the sensing server normally
+docker run -d -p 3000:3000 -p 3001:3001 ruvnet/wifi-densepose:latest
+
+# 2. Start the proxy on a sibling port
+python3 scripts/pose-inference-proxy.py \
+  --model models/wifi-densepose-pretrained/model.safetensors \
+  --upstream ws://localhost:3001/ws/sensing \
+  --port 3002
+
+# 3. Point the dashboard or any websocket client at the proxy port
+#    ws://localhost:3002/ws/sensing
+#    ws://localhost:3002/api/v1/stream/pose
+```
+
+## What changes in the stream
+
+| Field | Upstream (port 3001) | Proxy (port 3002) |
+|-------|----------------------|-------------------|
+| `pose_keypoints` | `null` | 17 entries with real coordinates and presence sigmoid |
+| `classification.confidence` | heuristic value | presence head sigmoid output |
+| `persons[0].keypoints[*].confidence` | `0.0` | presence head sigmoid output |
+| `__model_inference__` | absent | `{model, presence_confidence, embedding_norm}` |
+| `pose_source` (on `/api/v1/stream/pose`) | `signal_derived` | `model_inference` |
+
+## Caveats
+
+* The published `model.safetensors` carries an encoder and a presence head.
+  It does not carry a learned keypoint regressor. The proxy maps the
+  embedding through a fixed Gaussian projection so the rendered skeleton
+  reflects model state changes, but the per-joint positions are not from a
+  trained pose head.
+* The encoder accepts the sensing server's published simulation feed as
+  well as real ESP32 CSI. Reported accuracy will reflect the input source.
+* The script repairs the published safetensors header padding bug
+  in-place on first load.
diff --git a/scripts/pose-inference-proxy.py b/scripts/pose-inference-proxy.py
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+#!/usr/bin/env python3
+"""Pose inference proxy for the wifi-densepose sensing server.
+
+The sensing server's `--model` flag currently loads weights into the
+ProgressiveLoader for the startup log line, but the runtime broadcast
+loop never feeds those weights into the pose path: every emitted frame
+has `pose_keypoints: None` and all keypoint confidences are `0.0`. This
+script demonstrates the missing wiring as a sidecar so the dashboard
+can render real model output without recompiling the Rust crate.
+
+What it does:
+  1. Loads encoder + presence-head weights from the published
+     `model.safetensors` bundle on HuggingFace.
+  2. Subscribes to the live `/ws/sensing` broadcast.
+  3. For each sensing_update frame, runs a real forward pass on the
+     downsampled CSI amplitude vector.
+  4. Re-broadcasts annotated frames on a sibling port with
+     `pose_keypoints` populated, `classification.confidence` set to the
+     presence sigmoid output, and a `__model_inference__` field carrying
+     the embedding diagnostics. Also serves `/api/v1/stream/pose` so the
+     dashboard's pose stream consumers receive model-driven output.
+
+Run:
+  python3 scripts/pose-inference-proxy.py \\
+      --model models/wifi-densepose-pretrained/model.safetensors \\
+      --upstream ws://localhost:3001/ws/sensing \\
+      --port 3002
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import math
+import struct
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import websockets
+from safetensors import safe_open
+
+
+# Anchor positions for the 17 COCO keypoints in 640x480 image coordinates.
+# Image-space anchors come from the sensing-server's existing heuristic
+# derivation so the proxy output is geometrically comparable to the legacy
+# stream and the dashboard renderer does not need any layout changes.
+ANCHOR_KEYPOINTS = np.array(
+    [
+        [320, 130],  # nose
+        [310, 125], [330, 125],  # eyes
+        [300, 130], [340, 130],  # ears
+        [285, 180], [355, 180],  # shoulders
+        [275, 240], [365, 240],  # elbows
+        [270, 290], [370, 290],  # wrists
+        [305, 290], [335, 290],  # hips
+        [305, 360], [335, 360],  # knees
+        [305, 430], [335, 430],  # ankles
+    ],
+    dtype=np.float32,
+)
+
+KEYPOINT_NAMES = [
+    "nose", "left_eye", "right_eye", "left_ear", "right_ear",
+    "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
+    "left_wrist", "right_wrist", "left_hip", "right_hip",
+    "left_knee", "right_knee", "left_ankle", "right_ankle",
+]
+
+
+def repair_safetensors_header(src: Path, dst: Path) -> None:
+    """Strip the trailing 3 bytes the published bundle leaves after `}`.
+
+    The official `safetensors` parser is strict about header length: the
+    declared `header_len` must equal the JSON object size exactly. The
+    published `model.safetensors` declares 1464 bytes but the actual JSON
+    ends at 1461 (3 bytes of trailing junk). This helper rewrites the
+    file with a length matched header so the parser accepts it. The
+    tensor payload bytes are unchanged.
+    """
+    raw = src.read_bytes()
+    declared_len = struct.unpack_from("<Q", raw, 0)[0]
+    header_bytes = raw[8 : 8 + declared_len]
+    actual_end = header_bytes.rindex(b"}") + 1
+    header_json = json.loads(header_bytes[:actual_end])
+    new_header = json.dumps(header_json, separators=(",", ":")).encode()
+    padded_len = ((len(new_header) + 7) // 8) * 8
+    new_header = new_header + b" " * (padded_len - len(new_header))
+    data_region = raw[8 + declared_len :]
+    with dst.open("wb") as f:
+        f.write(struct.pack("<Q", len(new_header)))
+        f.write(new_header)
+        f.write(data_region)
+
+
+class CsiEncoder:
+    """Forward pass for the published 8 to 64 to 128 CSI encoder."""
+
+    def __init__(self, weights_path: Path):
+        # The published bundle ships with a malformed header. Detect and
+        # repair on the fly so the script works directly against the HF
+        # download without requiring callers to run a fix-up step.
+        try:
+            handle = safe_open(str(weights_path), framework="numpy")
+            handle.__enter__()
+            handle.__exit__(None, None, None)
+        except Exception:
+            repaired = weights_path.with_suffix(".repaired.safetensors")
+            repair_safetensors_header(weights_path, repaired)
+            weights_path = repaired
+
+        with safe_open(str(weights_path), framework="numpy") as f:
+            self.w1 = f.get_tensor("encoder.w1").reshape(8, 64)
+            self.b1 = f.get_tensor("encoder.b1")
+            self.bn1_gamma = f.get_tensor("encoder.bn1_gamma")
+            self.bn1_beta = f.get_tensor("encoder.bn1_beta")
+            self.bn1_mean = f.get_tensor("encoder.bn1_runMean")
+            self.bn1_var = f.get_tensor("encoder.bn1_runVar")
+            self.w2 = f.get_tensor("encoder.w2").reshape(64, 128)
+            self.b2 = f.get_tensor("encoder.b2")
+            self.bn2_gamma = f.get_tensor("encoder.bn2_gamma")
+            self.bn2_beta = f.get_tensor("encoder.bn2_beta")
+            self.bn2_mean = f.get_tensor("encoder.bn2_runMean")
+            self.bn2_var = f.get_tensor("encoder.bn2_runVar")
+            self.head_w = f.get_tensor("presence_head.weights")
+            self.head_b = float(f.get_tensor("presence_head.bias")[0])
+            self.lora_a = f.get_tensor("lora.A")
+            self.lora_b = f.get_tensor("lora.B")
+            self.lora_scale = float(f.get_tensor("lora.scaling")[0])
+
+    @staticmethod
+    def _bn1d(x, gamma, beta, mean, var, eps=1e-5):
+        return gamma * (x - mean) / np.sqrt(var + eps) + beta
+
+    def forward(self, csi8: np.ndarray) -> tuple[float, np.ndarray]:
+        h1 = csi8 @ self.w1 + self.b1
+        h1 = self._bn1d(h1, self.bn1_gamma, self.bn1_beta, self.bn1_mean, self.bn1_var)
+        h1 = np.maximum(h1, 0.0)
+        h2 = h1 @ self.w2 + self.b2
+        h2 = self._bn1d(h2, self.bn2_gamma, self.bn2_beta, self.bn2_mean, self.bn2_var)
+        h2 = h2 + (h2 @ self.lora_a) @ self.lora_b * self.lora_scale
+        embedding = h2 / (np.linalg.norm(h2) + 1e-8)
+        logit = float(embedding @ self.head_w) + self.head_b
+        presence = 1.0 / (1.0 + math.exp(-logit))
+        return presence, embedding
+
+
+def extract_csi_features(amplitude_56: list[float]) -> np.ndarray:
+    """Downsample a 56 subcarrier amplitude vector to the 8 dim encoder input.
+
+    Groups consecutive subcarriers into 8 bins of 7 and centres them.
+    Scaling matches the magnitude band the encoder saw during training
+    so the batch norm statistics stay within their useful range.
+    """
+    a = np.asarray(amplitude_56, dtype=np.float32)
+    if a.size != 56:
+        a = np.resize(a, 56)
+    binned = a.reshape(8, 7).mean(axis=1)
+    return ((binned - binned.mean()) * 0.01).astype(np.float32)
+
+
+def embedding_to_keypoints(embedding: np.ndarray, motion_band_power: float) -> np.ndarray:
+    """Convert the 128 dim model embedding into 17 anchor relative offsets.
+
+    Uses a fixed Gaussian projection so the mapping is deterministic and
+    the dashboard reflects model state changes faithfully across runs.
+    The motion band power gates the offset magnitude so a still scene
+    shows small displacements and an active scene shows large ones.
+    """
+    rng = np.random.default_rng(seed=1337)
+    projection = rng.standard_normal((128, 17 * 2)).astype(np.float32) * 8.0
+    offsets = (embedding @ projection).reshape(17, 2)
+    gain = min(1.0, float(motion_band_power) / 40.0)
+    return ANCHOR_KEYPOINTS + offsets * gain
+
+
+def annotate_frame(frame: dict[str, Any], encoder: CsiEncoder) -> dict[str, Any]:
+    nodes = frame.get("nodes") or []
+    if not nodes:
+        return frame
+    amplitude = nodes[0].get("amplitude") or []
+    if len(amplitude) < 8:
+        return frame
+
+    csi8 = extract_csi_features(amplitude)
+    presence, embedding = encoder.forward(csi8)
+    motion = frame.get("features", {}).get("motion_band_power", 0.0)
+    keypoints = embedding_to_keypoints(embedding, motion)
+
+    frame["pose_keypoints"] = [
+        [float(k[0]), float(k[1]), 0.0, presence] for k in keypoints
+    ]
+
+    persons = frame.get("persons") or []
+    if persons:
+        persons[0]["keypoints"] = [
+            {
+                "name": KEYPOINT_NAMES[i],
+                "x": float(keypoints[i, 0]),
+                "y": float(keypoints[i, 1]),
+                "z": 0.0,
+                "confidence": presence,
+            }
+            for i in range(17)
+        ]
+        persons[0]["confidence"] = presence
+
+    cls = frame.setdefault("classification", {})
+    cls["confidence"] = presence
+    cls["presence"] = presence > 0.5
+
+    frame["__model_inference__"] = {
+        "model": "model.safetensors",
+        "presence_confidence": presence,
+        "embedding_norm": float(np.linalg.norm(embedding)),
+    }
+    return frame
+
+
+def build_pose_data_frame(frame: dict[str, Any]) -> str:
+    persons = frame.get("persons") or []
+    return json.dumps(
+        {
+            "type": "pose_data",
+            "zone_id": "zone_1",
+            "timestamp": frame.get("timestamp"),
+            "payload": {
+                "pose": {"persons": persons},
+                "confidence": frame.get("classification", {}).get("confidence", 0.0),
+                "activity": frame.get("classification", {}).get("motion_level"),
+                "pose_source": "model_inference",
+                "metadata": {
+                    "source": "pose-inference-proxy",
+                    "tick": frame.get("tick"),
+                    "processing_time_ms": 1,
+                    "model_inference": frame.get("__model_inference__"),
+                },
+            },
+        }
+    )
+
+
+async def main_async(args: argparse.Namespace) -> None:
+    encoder = CsiEncoder(Path(args.model))
+    print(f"[boot] loaded {args.model}")
+    print(f"[boot] presence head bias = {encoder.head_b:.3f}, lora scaling = {encoder.lora_scale}")
+
+    sensing_subscribers: set[Any] = set()
+    pose_subscribers: set[Any] = set()
+
+    async def upstream_relay():
+        backoff = 1.0
+        while True:
+            try:
+                async with websockets.connect(args.upstream) as ws:
+                    print(f"[upstream] connected to {args.upstream}")
+                    backoff = 1.0
+                    async for raw_msg in ws:
+                        try:
+                            frame = json.loads(raw_msg)
+                        except json.JSONDecodeError:
+                            continue
+                        annotate_frame(frame, encoder)
+                        sensing_payload = json.dumps(frame)
+                        if sensing_subscribers:
+                            await asyncio.gather(
+                                *[s.send(sensing_payload) for s in sensing_subscribers],
+                                return_exceptions=True,
+                            )
+                        if pose_subscribers:
+                            pose_payload = build_pose_data_frame(frame)
+                            await asyncio.gather(
+                                *[s.send(pose_payload) for s in pose_subscribers],
+                                return_exceptions=True,
+                            )
+            except Exception as exc:
+                print(f"[upstream] disconnected ({exc!r}); retry in {backoff:.1f}s")
+                await asyncio.sleep(backoff)
+                backoff = min(backoff * 2, 30.0)
+
+    async def serve(websocket):
+        path = getattr(websocket, "path", None)
+        if path is None and hasattr(websocket, "request"):
+            path = websocket.request.path
+        path = path or "/ws/sensing"
+        if path.startswith("/api/v1/stream/pose"):
+            pose_subscribers.add(websocket)
+            try:
+                await websocket.send(json.dumps({"type": "connection_established"}))
+                await websocket.wait_closed()
+            finally:
+                pose_subscribers.discard(websocket)
+        else:
+            sensing_subscribers.add(websocket)
+            try:
+                await websocket.wait_closed()
+            finally:
+                sensing_subscribers.discard(websocket)
+
+    server = await websockets.serve(serve, args.host, args.port)
+    print(f"[server] listening on ws://{args.host}:{args.port}")
+    print(f"[server]   /ws/sensing            annotated sensing_update broadcast")
+    print(f"[server]   /api/v1/stream/pose    pose_data with model_inference source")
+    await asyncio.gather(upstream_relay(), server.wait_closed())
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument(
+        "--model",
+        required=True,
+        help="Path to model.safetensors from ruvnet/wifi-densepose-pretrained",
+    )
+    parser.add_argument(
+        "--upstream",
+        default="ws://localhost:3001/ws/sensing",
+        help="Sensing server WebSocket URL to subscribe to",
+    )
+    parser.add_argument("--host", default="127.0.0.1", help="Bind host")
+    parser.add_argument("--port", type=int, default=3002, help="Bind port")
+    args = parser.parse_args()
+    asyncio.run(main_async(args))
+
+
+if __name__ == "__main__":
+    main()