docs: add ADR-059 live ESP32 CSI pipeline + update README with demo links

- ADR-059: Documents end-to-end ESP32 → sensing server → browser pipeline - README: Add dual-modal pose fusion demo link, update ADR count to 49 - References issue #245 Co-Authored-By: claude-flow <ruv@ruv.net>
feat: live ESP32 CSI pipeline + auto-connect WebSocket
2026-06-13 10:53:20 +00:00 · 2026-03-12 17:40:16 -04:00 · 2026-03-12 17:37:27 -04:00 · 2026-03-12 16:16:07 -04:00 · 2026-03-12 16:10:29 -04:00 · 2026-03-12 14:26:39 -04:00
22 changed files with 231 additions and 4051 deletions
@@ -87,7 +87,7 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
  </a>
  <br>
  <em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em>
-  <br><br>
+  <br>
  <a href="https://ruvnet.github.io/RuView/"><strong>▶ Live Observatory Demo</strong></a>
  &nbsp;|&nbsp;
  <a href="https://ruvnet.github.io/RuView/pose-fusion.html"><strong>▶ Dual-Modal Pose Fusion Demo</strong></a>
@@ -1,59 +0,0 @@
-# ADR-060: Provision Channel Override and MAC Address Filtering
-
- **Status:** Accepted
- **Date:** 2026-03-12
- **Issues:** [#247](https://github.com/ruvnet/RuView/issues/247), [#229](https://github.com/ruvnet/RuView/issues/229)
-
-## Context
-
-Two related provisioning gaps were reported by users:
-
-1. **Channel mismatch (Issue #247):** The CSI collector initializes on the
-   Kconfig default channel (typically 6), even when the ESP32 connects to an AP
-   on a different channel (e.g. 11). On managed networks where the user cannot
-   change the router channel, this makes nodes undiscoverable. The
-   `provision.py` script has no `--channel` argument.
-
-2. **Missing MAC filter (Issue #229):** The v0.2.0 release notes documented a
-   `--filter-mac` argument for `provision.py`, but it was never implemented.
-   The firmware's CSI callback accepts frames from all sources, causing signal
-   mixing in multi-AP environments.
-
-## Decision
-
-### Channel configuration
-
- Add `--channel` argument to `provision.py` that writes a `csi_channel` key
-  (u8) to NVS.
- In `nvs_config.c`, read the `csi_channel` key and override
-  `channel_list[0]` when present.
- In `csi_collector_init()`, after WiFi connects, auto-detect the AP channel
-  via `esp_wifi_sta_get_ap_info()` and use it as the default CSI channel when
-  no NVS override is set. This ensures the CSI collector always matches the
-  connected AP's channel without requiring manual provisioning.
-
-### MAC address filtering
-
- Add `--filter-mac` argument to `provision.py` that writes a `filter_mac`
-  key (6-byte blob) to NVS.
- In `nvs_config.h`, add a `filter_mac[6]` field and `filter_mac_set` flag.
- In `nvs_config.c`, read the `filter_mac` blob from NVS.
- In the CSI callback (`wifi_csi_callback`), if `filter_mac_set` is true,
-  compare the source MAC from the received frame against the configured MAC
-  and drop non-matching frames.
-
-### Provisioning flow
-
-```
-python provision.py --port COM7 --channel 11
-python provision.py --port COM7 --filter-mac "AA:BB:CC:DD:EE:FF"
-python provision.py --port COM7 --channel 11 --filter-mac "AA:BB:CC:DD:EE:FF"
-```
-
-## Consequences
-
- Users on managed networks can force the CSI channel to match their AP
- Multi-AP environments can filter CSI to a single source
- Auto-channel detection eliminates the most common misconfiguration
- Backward compatible: existing provisioned nodes without these keys behave
-  as before (use Kconfig default channel, accept all MACs)
@@ -12,7 +12,6 @@
 */

 #include "csi_collector.h"
-#include "nvs_config.h"
 #include "stream_sender.h"
 #include "edge_processing.h"

@@ -22,9 +21,6 @@
 #include "esp_timer.h"
 #include "sdkconfig.h"

-/* ADR-060: Access the global NVS config for MAC filter and channel override. */
-extern nvs_config_t g_nvs_config;
-
 /* ADR-057: Build-time guard — fail early if CSI is not enabled in sdkconfig.
 * Without this, the firmware compiles but crashes at runtime with:
 *   "E (xxxx) wifi:CSI not enabled in menuconfig!"
@@ -155,14 +151,6 @@ size_t csi_serialize_frame(const wifi_csi_info_t *info, uint8_t *buf, size_t buf
 static void wifi_csi_callback(void *ctx, wifi_csi_info_t *info)
 {
    (void)ctx;
-
-    /* ADR-060: MAC address filtering — drop frames from non-matching sources. */
-    if (g_nvs_config.filter_mac_set) {
-        if (memcmp(info->mac, g_nvs_config.filter_mac, 6) != 0) {
-            return;  /* Source MAC doesn't match filter — skip frame. */
-        }
-    }
-
    s_cb_count++;

    if (s_cb_count <= 3 || (s_cb_count % 100) == 0) {
@@ -215,29 +203,6 @@ static void wifi_promiscuous_cb(void *buf, wifi_promiscuous_pkt_type_t type)

 void csi_collector_init(void)
 {
-    /* ADR-060: Determine the CSI channel.
-     * Priority: 1) NVS override (--channel), 2) connected AP channel, 3) Kconfig default. */
-    uint8_t csi_channel = (uint8_t)CONFIG_CSI_WIFI_CHANNEL;
-
-    if (g_nvs_config.csi_channel > 0) {
-        /* Explicit NVS override via provision.py --channel */
-        csi_channel = g_nvs_config.csi_channel;
-        ESP_LOGI(TAG, "Using NVS channel override: %u", (unsigned)csi_channel);
-    } else {
-        /* Auto-detect from connected AP */
-        wifi_ap_record_t ap_info;
-        if (esp_wifi_sta_get_ap_info(&ap_info) == ESP_OK && ap_info.primary > 0) {
-            csi_channel = ap_info.primary;
-            ESP_LOGI(TAG, "Auto-detected AP channel: %u", (unsigned)csi_channel);
-        } else {
-            ESP_LOGW(TAG, "Could not detect AP channel, using Kconfig default: %u",
-                     (unsigned)csi_channel);
-        }
-    }
-
-    /* Update the hop table's first channel to match. */
-    s_hop_channels[0] = csi_channel;
-
    /* Enable promiscuous mode — required for reliable CSI callbacks.
     * Without this, CSI only fires on frames destined to this station,
     * which may be very infrequent on a quiet network. */
@@ -265,15 +230,8 @@ void csi_collector_init(void)
    ESP_ERROR_CHECK(esp_wifi_set_csi_rx_cb(wifi_csi_callback, NULL));
    ESP_ERROR_CHECK(esp_wifi_set_csi(true));

-    if (g_nvs_config.filter_mac_set) {
-        ESP_LOGI(TAG, "MAC filter active: %02x:%02x:%02x:%02x:%02x:%02x",
-                 g_nvs_config.filter_mac[0], g_nvs_config.filter_mac[1],
-                 g_nvs_config.filter_mac[2], g_nvs_config.filter_mac[3],
-                 g_nvs_config.filter_mac[4], g_nvs_config.filter_mac[5]);
-    }
-
-    ESP_LOGI(TAG, "CSI collection initialized (node_id=%d, channel=%u)",
-             CONFIG_CSI_NODE_ID, (unsigned)csi_channel);
+    ESP_LOGI(TAG, "CSI collection initialized (node_id=%d, channel=%d)",
+             CONFIG_CSI_NODE_ID, CONFIG_CSI_WIFI_CHANNEL);
 }

 /* ---- ADR-029: Channel hopping ---- */
@@ -91,11 +91,6 @@ void nvs_config_load(nvs_config_t *cfg)
    cfg->wasm_verify = 0;  /* Kconfig disabled signature verification. */
 #endif

-    /* ADR-060: Channel override and MAC filter defaults. */
-    cfg->csi_channel = 0;  /* 0 = auto-detect from connected AP. */
-    cfg->filter_mac_set = 0;
-    memset(cfg->filter_mac, 0, 6);
-
    /* Try to override from NVS */
    nvs_handle_t handle;
    esp_err_t err = nvs_open("csi_cfg", NVS_READONLY, &handle);
@@ -282,26 +277,6 @@ void nvs_config_load(nvs_config_t *cfg)
        ESP_LOGW(TAG, "wasm_verify=1 but no wasm_pubkey in NVS — uploads will be rejected");
    }

-    /* ADR-060: CSI channel override. */
-    uint8_t csi_ch_val;
-    if (nvs_get_u8(handle, "csi_channel", &csi_ch_val) == ESP_OK) {
-        if ((csi_ch_val >= 1 && csi_ch_val <= 14) || (csi_ch_val >= 36 && csi_ch_val <= 177)) {
-            cfg->csi_channel = csi_ch_val;
-            ESP_LOGI(TAG, "NVS override: csi_channel=%u", (unsigned)cfg->csi_channel);
-        } else {
-            ESP_LOGW(TAG, "NVS csi_channel=%u invalid, ignored", (unsigned)csi_ch_val);
-        }
-    }
-
-    /* ADR-060: MAC address filter (6-byte blob). */
-    size_t mac_len = 6;
-    if (nvs_get_blob(handle, "filter_mac", cfg->filter_mac, &mac_len) == ESP_OK && mac_len == 6) {
-        cfg->filter_mac_set = 1;
-        ESP_LOGI(TAG, "NVS override: filter_mac=%02x:%02x:%02x:%02x:%02x:%02x",
-                 cfg->filter_mac[0], cfg->filter_mac[1], cfg->filter_mac[2],
-                 cfg->filter_mac[3], cfg->filter_mac[4], cfg->filter_mac[5]);
-    }
-
    /* Validate tdm_slot_index < tdm_node_count */
    if (cfg->tdm_slot_index >= cfg->tdm_node_count) {
        ESP_LOGW(TAG, "tdm_slot_index=%u >= tdm_node_count=%u, clamping to 0",
@@ -50,11 +50,6 @@ typedef struct {
    uint8_t  wasm_verify;                    /**< Require Ed25519 signature for uploads. */
    uint8_t  wasm_pubkey[32];               /**< Ed25519 public key for WASM signature. */
    uint8_t  wasm_pubkey_valid;             /**< 1 if pubkey was loaded from NVS. */
-
-    /* ADR-060: Channel override and MAC address filtering */
-    uint8_t  csi_channel;                    /**< Explicit CSI channel override (0 = auto-detect). */
-    uint8_t  filter_mac[6];                  /**< MAC address to filter CSI frames. */
-    uint8_t  filter_mac_set;                 /**< 1 if filter_mac was loaded from NVS. */
 } nvs_config_t;

 /**
@@ -64,13 +64,6 @@ def build_nvs_csv(args):
        writer.writerow(["vital_int", "data", "u16", str(args.vital_int)])
    if args.subk_count is not None:
        writer.writerow(["subk_count", "data", "u8", str(args.subk_count)])
-    # ADR-060: Channel override and MAC filter
-    if args.channel is not None:
-        writer.writerow(["csi_channel", "data", "u8", str(args.channel)])
-    if args.filter_mac is not None:
-        mac_bytes = bytes(int(b, 16) for b in args.filter_mac.split(":"))
-        # NVS blob: write as hex-encoded string for CSV compatibility
-        writer.writerow(["filter_mac", "data", "hex2bin", mac_bytes.hex()])
    return buf.getvalue()


@@ -172,10 +165,6 @@ def main():
    parser.add_argument("--vital-win", type=int, help="Phase history window in frames (default: 300)")
    parser.add_argument("--vital-int", type=int, help="Vitals packet interval in ms (default: 1000)")
    parser.add_argument("--subk-count", type=int, help="Top-K subcarrier count (default: 32)")
-    # ADR-060: Channel override and MAC filter
-    parser.add_argument("--channel", type=int, help="CSI channel (1-14 for 2.4GHz, 36-177 for 5GHz). "
-                        "Overrides auto-detection from connected AP.")
-    parser.add_argument("--filter-mac", type=str, help="MAC address to filter CSI frames (AA:BB:CC:DD:EE:FF)")
    parser.add_argument("--dry-run", action="store_true", help="Generate NVS binary but don't flash")

    args = parser.parse_args()
@@ -187,7 +176,6 @@ def main():
        args.edge_tier is not None, args.pres_thresh is not None,
        args.fall_thresh is not None, args.vital_win is not None,
        args.vital_int is not None, args.subk_count is not None,
-        args.channel is not None, args.filter_mac is not None,
    ])
    if not has_value:
        parser.error("At least one config value must be specified")
@@ -198,22 +186,6 @@ def main():
    if args.tdm_slot is not None and args.tdm_slot >= args.tdm_total:
        parser.error(f"--tdm-slot ({args.tdm_slot}) must be less than --tdm-total ({args.tdm_total})")

-    # ADR-060: Validate channel and MAC filter
-    if args.channel is not None:
-        if not ((1 <= args.channel <= 14) or (36 <= args.channel <= 177)):
-            parser.error(f"--channel must be 1-14 (2.4GHz) or 36-177 (5GHz), got {args.channel}")
-    if args.filter_mac is not None:
-        parts = args.filter_mac.split(":")
-        if len(parts) != 6:
-            parser.error(f"--filter-mac must be in AA:BB:CC:DD:EE:FF format, got '{args.filter_mac}'")
-        try:
-            for p in parts:
-                val = int(p, 16)
-                if val < 0 or val > 255:
-                    raise ValueError
-        except ValueError:
-            parser.error(f"--filter-mac contains invalid hex bytes: '{args.filter_mac}'")
-
    print("Building NVS configuration:")
    if args.ssid:
        print(f"  WiFi SSID:     {args.ssid}")
@@ -240,10 +212,6 @@ def main():
        print(f"  Vital Interval:{args.vital_int} ms")
    if args.subk_count is not None:
        print(f"  Top-K Subcarr: {args.subk_count}")
-    if args.channel is not None:
-        print(f"  CSI Channel:   {args.channel}")
-    if args.filter_mac is not None:
-        print(f"  Filter MAC:    {args.filter_mac}")

    csv_content = build_nvs_csv(args)

@@ -3,15 +3,15 @@
 <head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  <title>RuView — Dual-Modal Pose Estimation</title>
-  <link rel="stylesheet" href="pose-fusion/css/style.css?v=13">
+  <title>WiFi-DensePose — Dual-Modal Pose Estimation</title>
+  <link rel="stylesheet" href="pose-fusion/css/style.css">
 </head>
 <body>

  <!-- Header -->
  <header class="header">
    <div class="header-left">
-      <div class="logo"><span class="pi">&pi;</span> RuView</div>
+      <div class="logo"><span class="pi">&pi;</span> DensePose</div>
      <div class="header-title">Dual-Modal Pose Estimation — Live Video + WiFi CSI Fusion</div>
    </div>
    <div class="header-right">
@@ -40,7 +40,6 @@
      <div class="video-overlay-label" id="mode-label">DUAL FUSION</div>

      <div id="camera-prompt" class="camera-prompt">
-        <div class="camera-prompt-label" id="prompt-mode-label">DUAL FUSION</div>
        <p>Enable your webcam for live video pose estimation.<br>
           Or switch to <strong>CSI Only</strong> mode for WiFi-based sensing.</p>
        <button id="start-camera-btn">Enable Camera</button>
@@ -79,24 +78,7 @@
      <div class="panel">
        <div class="panel-title">&#9670; CSI Amplitude Heatmap</div>
        <div class="csi-canvas-wrapper">
-          <canvas id="csi-canvas" width="320" height="100"></canvas>
-        </div>
-      </div>
-
-      <!-- RSSI Signal Strength -->
-      <div class="panel">
-        <div class="panel-title">&#9670; RSSI Signal Strength</div>
-        <div class="rssi-row">
-          <div class="rssi-gauge">
-            <div class="rssi-bar-track">
-              <div class="rssi-bar-fill" id="rssi-bar" style="width:0%"></div>
-            </div>
-            <div class="rssi-values">
-              <span class="rssi-dbm" id="rssi-value">-- dBm</span>
-              <span class="rssi-quality" id="rssi-quality">--</span>
-            </div>
-          </div>
-          <canvas id="rssi-sparkline" width="160" height="32"></canvas>
+          <canvas id="csi-canvas" width="320" height="120"></canvas>
        </div>
      </div>

@@ -104,30 +86,7 @@
      <div class="panel">
        <div class="panel-title">&#9670; Embedding Space (2D Projection)</div>
        <div class="embedding-canvas-wrapper">
-          <canvas id="embedding-canvas" width="320" height="100"></canvas>
-        </div>
-      </div>
-
-      <!-- RuVector Attention Pipeline -->
-      <div class="panel">
-        <div class="panel-title">&#9670; RuVector WASM Attention Pipeline</div>
-        <div class="rv-pipeline">
-          <div class="rv-stage" id="rv-flash">Flash</div>
-          <div class="rv-arrow">&rarr;</div>
-          <div class="rv-stage" id="rv-mha">MHA</div>
-          <div class="rv-arrow">&rarr;</div>
-          <div class="rv-stage" id="rv-hyp">Hyper</div>
-          <div class="rv-arrow">&rarr;</div>
-          <div class="rv-stage" id="rv-lin">Linear</div>
-          <div class="rv-arrow">&rarr;</div>
-          <div class="rv-stage" id="rv-moe">MoE</div>
-          <div class="rv-arrow">&rarr;</div>
-          <div class="rv-stage" id="rv-lg">L+G</div>
-        </div>
-        <div class="rv-stats">
-          <span>Energy: <span id="rv-energy" style="color:var(--green-glow)">--</span></span>
-          <span>Refinement: <span id="rv-refine" style="color:var(--cyan)">--</span></span>
-          <span>Pose Impact: <span id="rv-impact" style="color:var(--amber)">--</span></span>
+          <canvas id="embedding-canvas" width="320" height="140"></canvas>
        </div>
      </div>

@@ -184,18 +143,18 @@
    <!-- Bottom Bar -->
    <div class="bottom-bar">
      <div>
-        RuView &middot; Dual-Modal Pose Estimation &middot;
-        Architecture: Conv2D &rarr; RuVector 6-Stage Attention (Flash+MHA+Hyperbolic+Linear+MoE+L/G) &rarr; Fusion &rarr; 26-Keypoint Pose
+        WiFi-DensePose &middot; Dual-Modal Pose Estimation &middot;
+        Architecture: MobileNet-V3 &times; 2 &rarr; Attention Fusion &rarr; 17-Keypoint COCO
      </div>
      <div>
-        <a href="https://github.com/ruvnet/RuView">GitHub</a> &middot;
-        CNN: <span id="cnn-backend">ruvector-cnn (loading…)</span> &middot;
+        <a href="https://github.com/ruvnet/wifi-densepose">GitHub</a> &middot;
+        CNN: ruvector-cnn (JS fallback) &middot;
        <a href="observatory.html">Observatory</a>
      </div>
    </div>

  </div><!-- /main-grid -->

-  <script type="module" src="pose-fusion/js/main.js?v=13"></script>
+  <script type="module" src="pose-fusion/js/main.js"></script>
 </body>
 </html>
@@ -1,4 +1,4 @@
-/* RuView — Dual-Modal Pose Fusion Demo
+/* WiFi-DensePose — Dual-Modal Pose Fusion Demo
   Dark theme matching Observatory */

@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=JetBrains+Mono:wght@400;600&display=swap');
@@ -136,14 +136,6 @@ body {
  overflow: hidden;
 }

-.video-panel {
-  grid-row: 1;
-}
-
-.side-panels {
-  grid-row: 1;
-}
-
 /* === Video Panel === */
 .video-panel {
  position: relative;
@@ -184,20 +176,14 @@ body {

 .camera-prompt {
  position: absolute;
-  top: 0; left: 0; right: 0; bottom: 0;
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  justify-content: center;
+  top: 50%; left: 50%;
+  transform: translate(-50%, -50%);
  text-align: center;
  color: var(--text-secondary);
-  padding: 24px;
-  z-index: 6;
-  background: radial-gradient(ellipse at center, rgba(0,210,120,0.08) 0%, rgba(8,12,20,0.95) 70%);
 }

 .camera-prompt button {
-  margin-top: 16px;
+  margin-top: 12px;
  padding: 10px 24px;
  background: var(--green-glow);
  color: #000;
@@ -212,34 +198,20 @@ body {

 .camera-prompt button:hover { background: var(--green-bright); }

-.camera-prompt-label {
-  font-family: 'JetBrains Mono', monospace;
-  font-size: 14px;
-  font-weight: 600;
-  letter-spacing: 2px;
-  color: var(--green-glow);
-  text-shadow: 0 0 12px rgba(0,216,120,0.4);
-  margin-bottom: 12px;
-}
-
 /* === Side Panels === */
 .side-panels {
  display: flex;
  flex-direction: column;
-  gap: 8px;
+  gap: 12px;
  overflow-y: auto;
  min-height: 0;
-  max-height: 100%;
-  scrollbar-width: thin;
-  scrollbar-color: var(--green-dim) transparent;
 }

 .panel {
  background: var(--bg-panel);
  border: 1px solid var(--bg-panel-border);
  border-radius: var(--radius);
-  padding: 10px 14px;
-  flex-shrink: 0;
+  padding: 14px;
 }

 .panel-title {
@@ -324,44 +296,6 @@ body {
  display: block;
 }

-/* === RuVector Pipeline === */
-.rv-pipeline {
-  display: flex;
-  align-items: center;
-  gap: 2px;
-  margin-bottom: 8px;
-  flex-wrap: wrap;
-}
-
-.rv-stage {
-  font-family: 'JetBrains Mono', monospace;
-  font-size: 10px;
-  padding: 3px 6px;
-  border-radius: 3px;
-  background: rgba(0,210,120,0.12);
-  border: 1px solid rgba(0,210,120,0.3);
-  color: var(--green-glow);
-  transition: all 0.3s;
-}
-
-.rv-stage.active {
-  background: rgba(0,210,120,0.25);
-  box-shadow: 0 0 6px rgba(0,210,120,0.3);
-}
-
-.rv-arrow {
-  font-size: 10px;
-  color: var(--text-label);
-}
-
-.rv-stats {
-  display: flex;
-  gap: 12px;
-  font-family: 'JetBrains Mono', monospace;
-  font-size: 10px;
-  color: var(--text-secondary);
-}
-
 /* === Latency Panel === */
 .latency-grid {
  display: grid;
@@ -453,71 +387,6 @@ body {
  text-decoration: none;
 }

-/* === RSSI Signal Strength === */
-.rssi-row {
-  display: flex;
-  align-items: center;
-  gap: 12px;
-}
-
-.rssi-gauge { flex: 1; }
-
-.rssi-bar-track {
-  height: 8px;
-  background: rgba(255,255,255,0.06);
-  border-radius: 4px;
-  overflow: hidden;
-  position: relative;
-}
-
-.rssi-bar-fill {
-  height: 100%;
-  border-radius: 4px;
-  background: linear-gradient(90deg, var(--red-alert), var(--amber), var(--green-glow));
-  transition: width 0.4s ease;
-  position: relative;
-  box-shadow: 0 0 6px rgba(0,210,120,0.3);
-}
-
-.rssi-bar-fill::after {
-  content: '';
-  position: absolute;
-  top: 0; left: 0; right: 0; bottom: 0;
-  background: linear-gradient(90deg, transparent 0%, rgba(255,255,255,0.2) 50%, transparent 100%);
-  animation: rssi-shimmer 2s ease-in-out infinite;
-}
-
-@keyframes rssi-shimmer {
-  0% { transform: translateX(-100%); }
-  100% { transform: translateX(100%); }
-}
-
-.rssi-values {
-  display: flex;
-  justify-content: space-between;
-  margin-top: 4px;
-}
-
-.rssi-dbm {
-  font-family: 'JetBrains Mono', monospace;
-  font-size: 14px;
-  font-weight: 600;
-  color: var(--green-glow);
-}
-
-.rssi-quality {
-  font-family: 'JetBrains Mono', monospace;
-  font-size: 11px;
-  color: var(--text-secondary);
-  text-transform: uppercase;
-}
-
-#rssi-sparkline {
-  flex-shrink: 0;
-  border-radius: 4px;
-  background: rgba(0,0,0,0.3);
-}
-
 /* === Skeleton colors === */
 .skeleton-joint { fill: var(--green-glow); }
 .skeleton-limb { stroke: var(--green-bright); }
@@ -37,18 +37,12 @@ export class CanvasRenderer {
    const limbColor = color === 'amber' ? this.colors.csiLimb : this.colors.limb;
    const glowColor = color === 'amber' ? 'rgba(255,176,32,0.4)' : this.colors.jointGlow;

-    // Extended keypoint styling
-    const fingerColor = '#ff6ef0';    // Magenta for finger tips
-    const fingerGlow = 'rgba(255,110,240,0.4)';
-    const fingerLimb = 'rgba(255,110,240,0.5)';
-    const toeColor = '#6ef0ff';       // Cyan for toes
-    const neckColor = '#ffffff';       // White for neck
-
    ctx.clearRect(0, 0, width, height);

    if (!keypoints || keypoints.length === 0) return;

    // Draw limbs first (behind joints)
+    ctx.lineWidth = 3;
    ctx.lineCap = 'round';

    for (const [i, j] of SKELETON_CONNECTIONS) {
@@ -60,22 +54,18 @@ export class CanvasRenderer {
      const bx = kpB.x * width, by = kpB.y * height;
      const avgConf = (kpA.confidence + kpB.confidence) / 2;

-      // Is this a hand/finger connection? (indices 17-22)
-      const isFingerLink = i >= 17 && i <= 22 || j >= 17 && j <= 22;
-      const isToeLink = i >= 23 && i <= 24 || j >= 23 && j <= 24;
-
      // Glow
-      ctx.strokeStyle = isFingerLink ? fingerLimb : this.colors.limbGlow;
-      ctx.lineWidth = isFingerLink ? 4 : 8;
-      ctx.globalAlpha = avgConf * (isFingerLink ? 0.3 : 0.4);
+      ctx.strokeStyle = this.colors.limbGlow;
+      ctx.lineWidth = 8;
+      ctx.globalAlpha = avgConf * 0.4;
      ctx.beginPath();
      ctx.moveTo(ax, ay);
      ctx.lineTo(bx, by);
      ctx.stroke();

      // Main line
-      ctx.strokeStyle = isFingerLink ? fingerColor : isToeLink ? toeColor : limbColor;
-      ctx.lineWidth = isFingerLink || isToeLink ? 1.5 : 2.5;
+      ctx.strokeStyle = limbColor;
+      ctx.lineWidth = 2.5;
      ctx.globalAlpha = avgConf;
      ctx.beginPath();
      ctx.moveTo(ax, ay);
@@ -85,52 +75,43 @@ export class CanvasRenderer {

    // Draw joints
    ctx.globalAlpha = 1;
-    for (let idx = 0; idx < keypoints.length; idx++) {
-      const kp = keypoints[idx];
+    for (const kp of keypoints) {
      if (!kp || kp.confidence < minConf) continue;

      const x = kp.x * width;
      const y = kp.y * height;
-      const isFinger = idx >= 17 && idx <= 22;
-      const isToe = idx >= 23 && idx <= 24;
-      const isNeck = idx === 25;
-      const r = isFinger ? 2 + kp.confidence * 2 : isToe ? 2 : 3 + kp.confidence * 3;
-      const jColor = isFinger ? fingerColor : isToe ? toeColor : isNeck ? neckColor : jointColor;
-      const gColor = isFinger ? fingerGlow : glowColor;
+      const r = 3 + kp.confidence * 3;

      // Glow
      ctx.beginPath();
-      ctx.arc(x, y, r + (isFinger ? 3 : 4), 0, Math.PI * 2);
-      ctx.fillStyle = gColor;
-      ctx.globalAlpha = kp.confidence * (isFinger ? 0.5 : 0.6);
+      ctx.arc(x, y, r + 4, 0, Math.PI * 2);
+      ctx.fillStyle = glowColor;
+      ctx.globalAlpha = kp.confidence * 0.6;
      ctx.fill();

      // Joint dot
      ctx.beginPath();
      ctx.arc(x, y, r, 0, Math.PI * 2);
-      ctx.fillStyle = jColor;
+      ctx.fillStyle = jointColor;
      ctx.globalAlpha = kp.confidence;
      ctx.fill();

-      // White center (body joints only)
-      if (!isFinger && !isToe) {
-        ctx.beginPath();
-        ctx.arc(x, y, r * 0.4, 0, Math.PI * 2);
-        ctx.fillStyle = '#fff';
-        ctx.globalAlpha = kp.confidence * 0.8;
-        ctx.fill();
-      }
+      // White center
+      ctx.beginPath();
+      ctx.arc(x, y, r * 0.4, 0, Math.PI * 2);
+      ctx.fillStyle = '#fff';
+      ctx.globalAlpha = kp.confidence * 0.8;
+      ctx.fill();
    }

    ctx.globalAlpha = 1;

-    // Confidence label + keypoint count
+    // Confidence label
    if (opts.label) {
-      const visCount = keypoints.filter(kp => kp && kp.confidence >= minConf).length;
      ctx.font = '11px "JetBrains Mono", monospace';
      ctx.fillStyle = jointColor;
      ctx.globalAlpha = 0.8;
-      ctx.fillText(`${opts.label} · ${visCount} joints`, 8, height - 8);
+      ctx.fillText(opts.label, 8, height - 8);
      ctx.globalAlpha = 1;
    }
  }
@@ -204,63 +185,22 @@ export class CanvasRenderer {
    ctx.beginPath(); ctx.moveTo(w / 2, 0); ctx.lineTo(w / 2, h); ctx.stroke();
    ctx.beginPath(); ctx.moveTo(0, h / 2); ctx.lineTo(w, h / 2); ctx.stroke();

-    // Auto-scale: find max extent across all point sets
-    let maxExtent = 0.01;
-    for (const pts of [points.video, points.csi, points.fused]) {
-      if (!pts) continue;
-      for (const p of pts) {
-        if (!p) continue;
-        maxExtent = Math.max(maxExtent, Math.abs(p[0]), Math.abs(p[1]));
-      }
-    }
-    const scale = 0.42 / maxExtent; // Fill ~84% of half-width
-
    const drawPoints = (pts, color, size) => {
      if (!pts || pts.length === 0) return;
      const len = pts.length;
-
-      // Draw trail line connecting recent points
-      if (len >= 2) {
-        ctx.beginPath();
-        let started = false;
-        for (let i = 0; i < len; i++) {
-          const p = pts[i];
-          if (!p) continue;
-          const px = w / 2 + p[0] * scale * w;
-          const py = h / 2 + p[1] * scale * h;
-          if (px < -10 || px > w + 10 || py < -10 || py > h + 10) continue;
-          if (!started) { ctx.moveTo(px, py); started = true; }
-          else ctx.lineTo(px, py);
-        }
-        ctx.strokeStyle = color;
-        ctx.globalAlpha = 0.2;
-        ctx.lineWidth = 1;
-        ctx.stroke();
-      }
-
-      // Draw dots with glow on newest
      for (let i = 0; i < len; i++) {
        const p = pts[i];
        if (!p) continue;
-        const age = 1 - (i / len) * 0.7;
-        const px = w / 2 + p[0] * scale * w;
-        const py = h / 2 + p[1] * scale * h;
+        const age = 1 - (i / len) * 0.7; // Fade older points
+        const px = w / 2 + p[0] * w * 0.35;
+        const py = h / 2 + p[1] * h * 0.35;

-        if (px < -10 || px > w + 10 || py < -10 || py > h + 10) continue;
-
-        // Glow on newest point
-        if (i === len - 1) {
-          ctx.beginPath();
-          ctx.arc(px, py, size + 4, 0, Math.PI * 2);
-          ctx.fillStyle = color;
-          ctx.globalAlpha = 0.3;
-          ctx.fill();
-        }
+        if (px < 0 || px > w || py < 0 || py > h) continue;

        ctx.beginPath();
-        ctx.arc(px, py, i === len - 1 ? size + 1 : size, 0, Math.PI * 2);
+        ctx.arc(px, py, size, 0, Math.PI * 2);
        ctx.fillStyle = color;
-        ctx.globalAlpha = age * 0.8;
+        ctx.globalAlpha = age * 0.7;
        ctx.fill();
      }
    };
@@ -1,11 +1,10 @@
 /**
- * CNN Embedder — RuVector Attention-powered feature extractor.
+ * CNN Embedder — Lightweight MobileNet-V3-style feature extractor.
 *
- * Uses the real ruvector-attention-wasm WASM module for Multi-Head Attention
- * and Flash Attention on CSI/video data. Falls back to a JS Conv2D pipeline
- * when WASM is not available.
+ * Architecture mirrors ruvector-cnn: Conv2D → BatchNorm → ReLU → Pool → Project → L2 Normalize
+ * Uses pre-seeded random weights (deterministic). When ruvector-cnn-wasm is available,
+ * transparently delegates to the WASM implementation.
 *
- * Pipeline: Conv2D → BatchNorm → ReLU → Pool → RuVector Attention → Project → L2 Normalize
 * Two instances are created: one for video frames, one for CSI pseudo-images.
 */

@@ -32,14 +31,6 @@ export class CnnEmbedder {
    this.embeddingDim = opts.embeddingDim || 128;
    this.normalize = opts.normalize !== false;
    this.wasmEmbedder = null;
-    this.rvAttention = null;      // RuVector Multi-Head Attention (WASM)
-    this.rvFlash = null;          // RuVector Flash Attention (WASM)
-    this.rvHyperbolic = null;     // RuVector Hyperbolic Attention (hierarchical body)
-    this.rvMoE = null;            // RuVector Mixture-of-Experts (body-region routing)
-    this.rvLinear = null;         // RuVector Linear Attention (O(n) fast hand refinement)
-    this.rvLocalGlobal = null;    // RuVector Local-Global Attention (detail + context)
-    this.rvModule = null;         // RuVector WASM module reference
-    this.useRuVector = false;

    // Initialize weights with deterministic PRNG
    const rng = mulberry32(opts.seed || 42);
@@ -57,50 +48,18 @@ export class CnnEmbedder {
    this.bnMean = new Float32Array(16).fill(0.0);
    this.bnVar = new Float32Array(16).fill(1.0);

-    // Projection: 16 → embeddingDim (used when RuVector not available)
+    // Projection: 16 → embeddingDim
    this.projWeights = new Float32Array(16 * this.embeddingDim);
    for (let i = 0; i < this.projWeights.length; i++) {
      this.projWeights[i] = randRange(-0.1, 0.1);
    }
-
-    // Attention projection: attention_dim → embeddingDim
-    this.attnProjWeights = new Float32Array(16 * this.embeddingDim);
-    for (let i = 0; i < this.attnProjWeights.length; i++) {
-      this.attnProjWeights[i] = randRange(-0.08, 0.08);
-    }
  }

  /**
-   * Try to load RuVector attention WASM, then fall back to ruvector-cnn-wasm
+   * Try to load WASM embedder from ruvector-cnn-wasm package
   * @param {string} wasmPath - Path to the WASM package directory
   */
  async tryLoadWasm(wasmPath) {
-    // First try: RuVector Attention WASM (the real thing — browser ESM build)
-    try {
-      const attnBase = new URL('../pkg/ruvector-attention/ruvector_attention_browser.js', import.meta.url).href;
-      const mod = await import(attnBase);
-      await mod.default();  // async WASM init via fetch
-      mod.init();
-
-      // Create all 6 attention mechanisms
-      this.rvAttention = new mod.WasmMultiHeadAttention(16, 4);
-      this.rvFlash = new mod.WasmFlashAttention(16, 8);
-      this.rvHyperbolic = new mod.WasmHyperbolicAttention(16, -1.0);
-      this.rvMoE = new mod.WasmMoEAttention(16, 3, 2);
-      this.rvLinear = new mod.WasmLinearAttention(16, 16);
-      this.rvLocalGlobal = new mod.WasmLocalGlobalAttention(16, 4, 2);
-      this.rvModule = mod;
-      this.useRuVector = true;
-
-      // Log available mechanisms
-      const mechs = mod.available_mechanisms();
-      console.log(`[CNN] RuVector WASM v${mod.version()} — all 6 attention mechanisms active`, mechs);
-      return true;
-    } catch (e) {
-      console.log('[CNN] RuVector Attention WASM not available:', e.message);
-    }
-
-    // Second try: ruvector-cnn-wasm (legacy path)
    try {
      const mod = await import(`${wasmPath}/ruvector_cnn_wasm.js`);
      await mod.default();
@@ -109,10 +68,10 @@ export class CnnEmbedder {
      config.embedding_dim = this.embeddingDim;
      config.normalize = this.normalize;
      this.wasmEmbedder = new mod.WasmCnnEmbedder(config);
-      console.log('[CNN] WASM CNN embedder loaded successfully');
+      console.log('[CNN] WASM embedder loaded successfully');
      return true;
    } catch (e) {
-      console.log('[CNN] WASM CNN not available, using JS fallback:', e.message);
+      console.log('[CNN] WASM not available, using JS fallback:', e.message);
      return false;
    }
  }
@@ -166,17 +125,10 @@ export class CnnEmbedder {
      if (convOut[i] < 0) convOut[i] = 0;
    }

-    // 6. Global average pooling → spatial tokens (each 16-dim)
+    // 6. Global average pooling → 16-dim
    const outH = sz - 2, outW = sz - 2;
-    const spatial = outH * outW;
-
-    // 7. RuVector Attention (if loaded) — apply attention over spatial tokens
-    if (this.useRuVector && this.rvAttention) {
-      return this._extractWithAttention(convOut, spatial, 16);
-    }
-
-    // Fallback: simple global average pool + linear projection
    const pooled = new Float32Array(16);
+    const spatial = outH * outW;
    for (let i = 0; i < spatial; i++) {
      for (let c = 0; c < 16; c++) {
        pooled[c] += convOut[i * 16 + c];
@@ -184,7 +136,7 @@ export class CnnEmbedder {
    }
    for (let c = 0; c < 16; c++) pooled[c] /= spatial;

-    // Linear projection → embeddingDim
+    // 7. Linear projection → embeddingDim
    const emb = new Float32Array(this.embeddingDim);
    for (let o = 0; o < this.embeddingDim; o++) {
      let sum = 0;
@@ -194,7 +146,7 @@ export class CnnEmbedder {
      emb[o] = sum;
    }

-    // L2 normalize
+    // 8. L2 normalize
    if (this.normalize) {
      let norm = 0;
      for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
@@ -207,149 +159,6 @@ export class CnnEmbedder {
    return emb;
  }

-  /**
-   * Full 6-stage RuVector WASM attention pipeline:
-   * 1. Flash Attention (efficient O(n) pre-screening of spatial tokens)
-   * 2. Multi-Head Attention (global spatial reasoning)
-   * 3. Hyperbolic Attention (hierarchical body-part structure, Poincaré ball)
-   * 4. Linear Attention (O(n) refinement for fine detail — hands/extremities)
-   * 5. MoE Attention (body-region specialized expert routing)
-   * 6. Local-Global Attention (local detail + global context fusion)
-   * → Weighted blend + batch_normalize + project + L2 normalize
-   */
-  _extractWithAttention(convOut, numTokens, channels) {
-    const mod = this.rvModule;
-
-    // Subsample spatial tokens for attention (max 64 for speed)
-    const maxTokens = 64;
-    const step = numTokens > maxTokens ? Math.floor(numTokens / maxTokens) : 1;
-    const tokens = [];
-    for (let i = 0; i < numTokens && tokens.length < maxTokens; i += step) {
-      const token = new Float32Array(channels);
-      for (let c = 0; c < channels; c++) {
-        token[c] = convOut[i * channels + c];
-      }
-      tokens.push(token);
-    }
-
-    const numQueries = Math.min(4, tokens.length);
-    const queryStride = Math.floor(tokens.length / numQueries);
-
-    // === Stage 1: Flash Attention (efficient pre-screening) ===
-    const flashOut = new Float32Array(channels);
-    try {
-      // Flash attention with block size 8 for efficient O(n) screening
-      const result = this.rvFlash.compute(tokens[0], tokens, tokens);
-      for (let c = 0; c < channels; c++) flashOut[c] = result[c];
-    } catch (_) {
-      flashOut.set(tokens[0]);
-    }
-
-    // === Stage 2: Multi-Head Attention (global spatial reasoning) ===
-    const mhaOut = new Float32Array(channels);
-    for (let q = 0; q < numQueries; q++) {
-      const queryToken = tokens[q * queryStride];
-      try {
-        const result = this.rvAttention.compute(queryToken, tokens, tokens);
-        for (let c = 0; c < channels; c++) mhaOut[c] += result[c] / numQueries;
-      } catch (_) {
-        for (let c = 0; c < channels; c++) mhaOut[c] += queryToken[c] / numQueries;
-      }
-    }
-
-    // === Stage 3: Hyperbolic Attention (hierarchical body structure) ===
-    const hyOut = new Float32Array(channels);
-    try {
-      const result = this.rvHyperbolic.compute(mhaOut, tokens, tokens);
-      for (let c = 0; c < channels; c++) hyOut[c] = result[c];
-    } catch (_) {
-      hyOut.set(mhaOut);
-    }
-
-    // === Stage 4: Linear Attention (O(n) fast refinement for extremities) ===
-    const linOut = new Float32Array(channels);
-    try {
-      const result = this.rvLinear.compute(hyOut, tokens, tokens);
-      for (let c = 0; c < channels; c++) linOut[c] = result[c];
-    } catch (_) {
-      linOut.set(hyOut);
-    }
-
-    // === Stage 5: MoE Attention (body-region expert routing) ===
-    const moeOut = new Float32Array(channels);
-    try {
-      const result = this.rvMoE.compute(linOut, tokens, tokens);
-      for (let c = 0; c < channels; c++) moeOut[c] = result[c];
-    } catch (_) {
-      moeOut.set(linOut);
-    }
-
-    // === Stage 6: Local-Global Attention (detail + context) ===
-    const lgOut = new Float32Array(channels);
-    try {
-      const result = this.rvLocalGlobal.compute(moeOut, tokens, tokens);
-      for (let c = 0; c < channels; c++) lgOut[c] = result[c];
-    } catch (_) {
-      lgOut.set(moeOut);
-    }
-
-    // === Blend all 6 outputs ===
-    // Use WASM softmax on log-energy scores for dynamic stage weighting
-    const blended = new Float32Array(channels);
-    const stages = [flashOut, mhaOut, hyOut, linOut, moeOut, lgOut];
-    // Use log-energy to prevent exp() overflow in softmax
-    const logEnergies = new Float32Array(6);
-    for (let s = 0; s < 6; s++) {
-      const e = this._energy(stages[s]);
-      logEnergies[s] = e > 1e-10 ? Math.log(e) : -20;
-    }
-    try { mod.softmax(logEnergies); } catch (_) {
-      let max = -Infinity;
-      for (let i = 0; i < 6; i++) max = Math.max(max, logEnergies[i]);
-      let sum = 0;
-      for (let i = 0; i < 6; i++) { logEnergies[i] = Math.exp(logEnergies[i] - max); sum += logEnergies[i]; }
-      for (let i = 0; i < 6; i++) logEnergies[i] /= sum;
-    }
-    for (let c = 0; c < channels; c++) {
-      for (let s = 0; s < 6; s++) {
-        blended[c] += logEnergies[s] * stages[s][c];
-      }
-    }
-
-    // Batch normalize only when we have enough diversity (skip for single vectors)
-    // Single-vector batch norm collapses to zeros, killing embedding space
-    let normed = blended;
-
-    // Project to embeddingDim
-    const emb = new Float32Array(this.embeddingDim);
-    for (let o = 0; o < this.embeddingDim; o++) {
-      let sum = 0;
-      for (let i = 0; i < channels; i++) {
-        sum += normed[i] * this.attnProjWeights[i * this.embeddingDim + o];
-      }
-      emb[o] = sum;
-    }
-
-    // L2 normalize using RuVector WASM
-    if (this.normalize) {
-      try { mod.normalize(emb); } catch (_) {
-        let norm = 0;
-        for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
-        norm = Math.sqrt(norm);
-        if (norm > 1e-8) for (let i = 0; i < emb.length; i++) emb[i] /= norm;
-      }
-    }
-
-    return emb;
-  }
-
-  /** Compute vector energy (L2 norm squared) for attention weighting */
-  _energy(vec) {
-    let e = 0;
-    for (let i = 0; i < vec.length; i++) e += vec[i] * vec[i];
-    return e;
-  }
-
  _conv2d3x3(input, H, W, Cin, Cout) {
    const outH = H - 2, outW = W - 2;
    const output = new Float32Array(outH * outW * Cout);
@@ -401,33 +210,7 @@ export class CnnEmbedder {
    return output;
  }

-  /** Cosine similarity using WASM when available, JS fallback */
-  cosineSim(a, b) {
-    if (this.rvModule) {
-      try { return this.rvModule.cosine_similarity(a, b); } catch (_) { /* fallback */ }
-    }
-    return CnnEmbedder.cosineSimilarity(a, b);
-  }
-
-  /** L2 norm using WASM when available */
-  l2Norm(vec) {
-    if (this.rvModule) {
-      try { return this.rvModule.l2_norm(vec); } catch (_) { /* fallback */ }
-    }
-    let norm = 0;
-    for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i];
-    return Math.sqrt(norm);
-  }
-
-  /** Pairwise distance matrix using WASM (for skeleton validation) */
-  pairwiseDistances(vectors) {
-    if (this.rvModule) {
-      try { return this.rvModule.pairwise_distances(vectors); } catch (_) { /* fallback */ }
-    }
-    return null;
-  }
-
-  /** Static JS fallback for cosine similarity */
+  /** Cosine similarity between two embeddings */
  static cosineSimilarity(a, b) {
    let dot = 0, normA = 0, normB = 0;
    for (let i = 0; i < a.length; i++) {
@@ -9,8 +9,6 @@
 */

 export class CsiSimulator {
-  static VERSION = 'v4-drift';  // Cache-bust verification
-
  constructor(opts = {}) {
    this.subcarriers = opts.subcarriers || 52; // 802.11n HT20
    this.timeWindow = opts.timeWindow || 56;   // frames in sliding window
@@ -34,10 +32,6 @@ export class CsiSimulator {
      this._basePhase[i] = (i / this.subcarriers) * Math.PI * 2;
    }

-    // RSSI tracking
-    this.rssiDbm = -70; // default mid-range
-    this._rssiTarget = -70;
-
    // Person influence (updated from video motion)
    this.personPresence = 0;
    this.personX = 0.5;
@@ -79,9 +73,6 @@ export class CsiSimulator {
   * (simulating through-wall sensing capability).
   */
  updatePersonState(presence, x, y, motion) {
-    // Don't override real CSI sensing with synthetic video-derived state
-    if (this.mode === 'live') return;
-
    if (presence > 0.1) {
      // Person detected in video — update CSI state directly
      this.personPresence = presence;
@@ -135,13 +126,6 @@ export class CsiSimulator {
      this.phaseBuffer.shift();
    }

-    // RSSI: smooth toward target (demo mode generates synthetic RSSI)
-    if (this.mode === 'demo') {
-      // Simulate RSSI based on person presence and slow drift
-      this._rssiTarget = -55 - 25 * (1 - this.personPresence) + Math.sin(elapsed * 0.3) * 3;
-    }
-    this.rssiDbm += (this._rssiTarget - this.rssiDbm) * 0.1;
-
    // SNR estimate
    let signalPower = 0, noisePower = 0;
    for (let i = 0; i < this.subcarriers; i++) {
@@ -231,11 +215,6 @@ export class CsiSimulator {
      this._noiseState[i] = 0.95 * this._noiseState[i] + 0.05 * (rng() * 2 - 1) * 0.03;
      a += this._noiseState[i];

-      // Ambient temporal drift (multipath fading even in empty room)
-      a += 0.06 * Math.sin(elapsed * 0.7 + i * 0.25)
-         + 0.04 * Math.sin(elapsed * 1.3 - i * 0.18)
-         + 0.03 * Math.cos(elapsed * 2.1 + i * 0.4);
-
      // Person-induced CSI perturbation
      if (presence > 0.1) {
        // Subcarrier-dependent body reflection (Fresnel zone model)
@@ -258,23 +237,6 @@ export class CsiSimulator {
  }

  _handleLiveFrame(data) {
-    // Handle JSON text frames from the sensing server
-    if (typeof data === 'string') {
-      try {
-        const msg = JSON.parse(data);
-        this._handleJsonFrame(msg);
-      } catch (_) { /* ignore malformed JSON */ }
-      return;
-    }
-
-    // Handle Blob data (convert to ArrayBuffer and re-process)
-    if (data instanceof Blob) {
-      data.arrayBuffer().then(ab => this._handleLiveFrame(ab)).catch(() => {});
-      return;
-    }
-
-    // Handle binary ArrayBuffer frames (ADR-018 format)
-    if (!(data instanceof ArrayBuffer)) return;
    const view = new DataView(data);
    // Check ADR-018 magic: 0xC5110001
    if (data.byteLength < 20) return;
@@ -294,64 +256,6 @@ export class CsiSimulator {
    }
  }

-  _handleJsonFrame(msg) {
-    // Sensing server sends: { type: "sensing_update", nodes: [{ amplitude: [...], subcarrier_count }], classification, features }
-    this._liveAmplitude = new Float32Array(this.subcarriers);
-    this._livePhase = new Float32Array(this.subcarriers);
-
-    // Extract amplitude from sensing_update node data
-    const node = (msg.nodes && msg.nodes[0]) || msg;
-    const ampArr = node.amplitude || msg.amplitude;
-    if (ampArr && Array.isArray(ampArr)) {
-      const n = Math.min(ampArr.length, this.subcarriers);
-      // Server sends raw amplitude (already magnitude), normalize to 0-1
-      let maxAmp = 0;
-      for (let i = 0; i < n; i++) maxAmp = Math.max(maxAmp, Math.abs(ampArr[i]));
-      const scale = maxAmp > 0 ? 1.0 / maxAmp : 1.0;
-      for (let i = 0; i < n; i++) {
-        this._liveAmplitude[i] = Math.abs(ampArr[i]) * scale;
-      }
-    }
-
-    // Phase from node (if available)
-    const phaseArr = node.phase || msg.phase;
-    if (phaseArr && Array.isArray(phaseArr)) {
-      const n = Math.min(phaseArr.length, this.subcarriers);
-      for (let i = 0; i < n; i++) this._livePhase[i] = phaseArr[i];
-    } else if (ampArr) {
-      // Synthesize phase from amplitude variation (Hilbert-like estimate)
-      for (let i = 1; i < this.subcarriers; i++) {
-        this._livePhase[i] = this._livePhase[i - 1] + (this._liveAmplitude[i] - this._liveAmplitude[i - 1]) * Math.PI;
-      }
-    }
-
-    // Handle raw I/Q pairs
-    const iq = node.iq || msg.iq;
-    if (iq && Array.isArray(iq)) {
-      const n = Math.min(iq.length / 2, this.subcarriers);
-      for (let i = 0; i < n; i++) {
-        const real = iq[i * 2], imag = iq[i * 2 + 1];
-        this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048;
-        this._livePhase[i] = Math.atan2(imag, real);
-      }
-    }
-
-    // Extract RSSI from node data
-    if (typeof node.rssi_dbm === 'number') {
-      this._rssiTarget = node.rssi_dbm;
-    } else if (msg.features && typeof msg.features.mean_rssi === 'number') {
-      this._rssiTarget = msg.features.mean_rssi;
-    }
-
-    // Update presence from server classification
-    const cls = msg.classification;
-    if (cls) {
-      if (typeof cls.confidence === 'number') {
-        this.personPresence = cls.presence ? cls.confidence : 0;
-      }
-    }
-  }
-
  _mulberry32(seed) {
    return function() {
      let t = (seed += 0x6D2B79F5);
@@ -8,14 +8,12 @@
 export class FusionEngine {
  /**
   * @param {number} embeddingDim
-   * @param {object} opts
-   * @param {object} opts.wasmModule - RuVector WASM module for cosine_similarity etc.
   */
-  constructor(embeddingDim = 128, opts = {}) {
+  constructor(embeddingDim = 128) {
    this.embeddingDim = embeddingDim;
-    this.wasmModule = opts.wasmModule || null;

    // Learnable attention weights (initialized to balanced 0.5)
+    // In production, these would be loaded from trained JSON
    this.attentionWeights = new Float32Array(embeddingDim).fill(0.5);

    // Dynamic modality confidence [0, 1]
@@ -33,9 +31,6 @@ export class FusionEngine {
    this.maxHistory = 50;
  }

-  /** Set the WASM module reference (called after WASM loads) */
-  setWasmModule(mod) { this.wasmModule = mod; }
-
  /**
   * Update quality-based confidence scores
   * @param {number} videoBrightness - [0,1] video brightness quality
@@ -99,11 +94,12 @@ export class FusionEngine {
      fused[i] = alpha * videoEmb[i] + (1 - alpha) * csiEmb[i];
    }

-    // Re-normalize using WASM when available
-    if (this.wasmModule) {
-      try { this.wasmModule.normalize(fused); } catch (_) { this._jsNormalize(fused); }
-    } else {
-      this._jsNormalize(fused);
+    // Re-normalize
+    let norm = 0;
+    for (let i = 0; i < dim; i++) norm += fused[i] * fused[i];
+    norm = Math.sqrt(norm);
+    if (norm > 1e-8) {
+      for (let i = 0; i < dim; i++) fused[i] /= norm;
    }

    this._recordEmbedding(videoEmb, csiEmb, fused);
@@ -115,19 +111,18 @@ export class FusionEngine {
   * @returns {{ video: Array, csi: Array, fused: Array }}
   */
  getEmbeddingPoints() {
-    // Sparse random projection: pick a few dimensions with fixed coefficients
-    // to get visible 2D spread (avoids cancellation from summing all 128 dims)
+    // Simple 2D projection using first two principal components (approximated)
    const project = (emb) => {
      if (!emb || emb.length < 4) return null;
-      // Use 8 sparse dimensions with predetermined signs (seeded, not random)
-      const dim = emb.length;
-      const x = emb[0] * 3.2 - emb[3] * 2.8 + emb[7] * 2.1 - emb[12] * 1.9
-              + (dim > 30 ? emb[29] * 1.5 - emb[31] * 1.3 : 0)
-              + (dim > 60 ? emb[55] * 1.1 - emb[60] * 0.9 : 0);
-      const y = emb[1] * 3.0 - emb[5] * 2.5 + emb[9] * 2.3 - emb[15] * 1.7
-              + (dim > 40 ? emb[37] * 1.4 - emb[42] * 1.2 : 0)
-              + (dim > 80 ? emb[73] * 1.0 - emb[80] * 0.8 : 0);
-      return [x, y];
+      // Use pairs of dimensions as crude 2D projection
+      let x = 0, y = 0;
+      for (let i = 0; i < emb.length; i += 2) {
+        x += emb[i] * (i % 4 < 2 ? 1 : -1);
+        if (i + 1 < emb.length) {
+          y += emb[i + 1] * (i % 4 < 2 ? 1 : -1);
+        }
+      }
+      return [x * 2, y * 2]; // Scale for visibility
    };

    return {
@@ -146,11 +141,6 @@ export class FusionEngine {
    const c = this.recentCsiEmbeddings[this.recentCsiEmbeddings.length - 1];
    if (!v || !c) return 0;

-    // Use WASM cosine_similarity when available
-    if (this.wasmModule) {
-      try { return this.wasmModule.cosine_similarity(v, c); } catch (_) { /* fallback */ }
-    }
-
    let dot = 0, na = 0, nb = 0;
    for (let i = 0; i < v.length; i++) {
      dot += v[i] * c[i];
@@ -161,13 +151,6 @@ export class FusionEngine {
    return (na > 1e-8 && nb > 1e-8) ? dot / (na * nb) : 0;
  }

-  _jsNormalize(vec) {
-    let norm = 0;
-    for (let i = 0; i < vec.length; i++) norm += vec[i] * vec[i];
-    norm = Math.sqrt(norm);
-    if (norm > 1e-8) for (let i = 0; i < vec.length; i++) vec[i] /= norm;
-  }
-
  _recordEmbedding(video, csi, fused) {
    if (video) {
      this.recentVideoEmbeddings.push(new Float32Array(video));
@@ -1,15 +1,15 @@
 /**
- * RuView — Dual-Modal Pose Estimation Demo
+ * WiFi-DensePose — Dual-Modal Pose Estimation Demo
 *
 * Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
 */

-import { VideoCapture } from './video-capture.js?v=13';
-import { CsiSimulator } from './csi-simulator.js?v=13';
-import { CnnEmbedder } from './cnn-embedder.js?v=13';
-import { FusionEngine } from './fusion-engine.js?v=13';
-import { PoseDecoder } from './pose-decoder.js?v=13';
-import { CanvasRenderer } from './canvas-renderer.js?v=13';
+import { VideoCapture } from './video-capture.js';
+import { CsiSimulator } from './csi-simulator.js';
+import { CnnEmbedder } from './cnn-embedder.js';
+import { FusionEngine } from './fusion-engine.js';
+import { PoseDecoder } from './pose-decoder.js';
+import { CanvasRenderer } from './canvas-renderer.js';

 // === State ===
 let mode = 'dual';  // 'dual' | 'video' | 'csi'
@@ -71,20 +71,9 @@ const latTotalEl = document.getElementById('lat-total');
 // Cross-modal similarity
 const crossModalEl = document.getElementById('cross-modal-sim');

-// RSSI elements
-const rssiBarEl = document.getElementById('rssi-bar');
-const rssiValueEl = document.getElementById('rssi-value');
-const rssiQualityEl = document.getElementById('rssi-quality');
-const rssiSparkCanvas = document.getElementById('rssi-sparkline');
-const rssiSparkCtx = rssiSparkCanvas ? rssiSparkCanvas.getContext('2d') : null;
-const rssiHistory = [];
-const RSSI_HISTORY_MAX = 80;
-
 // === Initialize ===
 function init() {
-  console.log(`[PoseFusion] init() v4 — CsiSimulator=${CsiSimulator.VERSION || 'OLD'}, starting...`);
  resizeCanvases();
-  console.log(`[PoseFusion] canvases: skeleton=${skeletonCanvas.width}x${skeletonCanvas.height}, csi=${csiCanvas.width}x${csiCanvas.height}, emb=${embeddingCanvas.width}x${embeddingCanvas.height}`);
  window.addEventListener('resize', resizeCanvases);

  // Mode change
@@ -121,19 +110,10 @@ function init() {
    }
  });

-  // Try to load RuVector Attention WASM embedders (non-blocking)
-  const wasmBase = new URL('../pkg/ruvector-attention', import.meta.url).href;
-  visualCnn.tryLoadWasm(wasmBase).then((ok) => {
-    // Share the WASM module with FusionEngine for cosine_similarity, normalize, etc.
-    if (visualCnn.rvModule) fusionEngine.setWasmModule(visualCnn.rvModule);
-    // Update footer backend label
-    const backendEl = document.getElementById('cnn-backend');
-    if (backendEl) {
-      backendEl.textContent = ok && visualCnn.useRuVector
-        ? `RuVector WASM v${visualCnn.rvModule.version()} — 6 attention mechanisms`
-        : 'ruvector-cnn (JS fallback)';
-    }
-  });
+  // Try to load WASM embedders (non-blocking)
+  // Resolve relative to this JS module file (in pose-fusion/js/) → ../pkg/
+  const wasmBase = new URL('../pkg/ruvector_cnn_wasm', import.meta.url).href;
+  visualCnn.tryLoadWasm(wasmBase);
  csiCnn.tryLoadWasm(wasmBase);

  // Auto-connect to local sensing server WebSocket if available
@@ -170,6 +150,7 @@ async function startCamera() {

 function updateModeUI() {
  const needsVideo = mode !== 'csi';
+  const needsCsi = mode !== 'video';

  // Show/hide camera prompt
  if (needsVideo && !videoCapture.isActive) {
@@ -177,13 +158,6 @@ function updateModeUI() {
  } else {
    cameraPrompt.style.display = 'none';
  }
-
-  // Update mode label in both the overlay and the camera prompt
-  const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' };
-  const modeLabel = document.getElementById('mode-label');
-  const promptLabel = document.getElementById('prompt-mode-label');
-  if (modeLabel) modeLabel.textContent = labelMap[mode] || mode;
-  if (promptLabel) promptLabel.textContent = labelMap[mode] || mode;
 }

 function resizeCanvases() {
@@ -194,25 +168,22 @@ function resizeCanvases() {
    skeletonCanvas.height = rect.height;
  }

-  // CSI canvas (min 200px width)
-  csiCanvas.width = Math.max(200, csiCanvas.parentElement.clientWidth);
+  // CSI canvas
+  csiCanvas.width = csiCanvas.parentElement.clientWidth;
  csiCanvas.height = 120;

-  // Embedding canvas (min 200px width)
-  embeddingCanvas.width = Math.max(200, embeddingCanvas.parentElement.clientWidth);
+  // Embedding canvas
+  embeddingCanvas.width = embeddingCanvas.parentElement.clientWidth;
  embeddingCanvas.height = 140;
 }

 // === Main Loop ===
-let _loopErrorShown = false;
-let _diagDone = false;
 function mainLoop(timestamp) {
  if (!isRunning) return;
  requestAnimationFrame(mainLoop);

  if (isPaused) return;

-  try {
  const elapsed = performance.now() / 1000 - startTime;
  const totalStart = performance.now();

@@ -338,134 +309,6 @@ function mainLoop(timestamp) {
  // Cross-modal similarity
  const sim = fusionEngine.getCrossModalSimilarity();
  crossModalEl.textContent = sim.toFixed(3);
-
-  // RuVector attention pipeline stats
-  const rvStats = poseDecoder.attentionStats;
-  const rvEnergyEl = document.getElementById('rv-energy');
-  const rvRefineEl = document.getElementById('rv-refine');
-  const rvImpactEl = document.getElementById('rv-impact');
-  if (rvEnergyEl) rvEnergyEl.textContent = (rvStats.energy || 0).toFixed(2);
-  if (rvRefineEl) rvRefineEl.textContent = ((rvStats.refinementMag || 0) * 1000).toFixed(1) + 'px';
-  if (rvImpactEl) {
-    const impact = Math.min(100, (rvStats.refinementMag || 0) * 5000);
-    rvImpactEl.textContent = impact.toFixed(0) + '%';
-  }
-  // Pulse the pipeline stages when active
-  if (visualCnn.useRuVector && rvStats.energy > 0.1) {
-    document.querySelectorAll('.rv-stage').forEach(el => el.classList.add('active'));
-  }
-
-  // RSSI update
-  updateRssi(csiSimulator.rssiDbm);
-
-  // One-time diagnostic
-  if (!_diagDone) {
-    _diagDone = true;
-    console.log(`[PoseFusion] frame 1 OK — mode=${mode}, csi.bufLen=${csiSimulator.amplitudeBuffer.length}, embPts=${embPoints?.fused?.length ?? 0}, rssi=${(csiSimulator.rssiDbm ?? -99).toFixed(1)}`);
-  }
-
-  } catch (err) {
-    if (!_loopErrorShown) {
-      _loopErrorShown = true;
-      console.error('[MainLoop]', err);
-      // Show error visually on page
-      const errDiv = document.createElement('div');
-      errDiv.style.cssText = 'position:fixed;bottom:60px;left:24px;right:24px;background:rgba(255,48,64,0.95);color:#fff;padding:12px 16px;border-radius:8px;font:12px/1.4 "JetBrains Mono",monospace;z-index:9999;max-height:120px;overflow:auto';
-      errDiv.textContent = `[MainLoop Error] ${err.message}\n${err.stack?.split('\n').slice(0,3).join('\n')}`;
-      document.body.appendChild(errDiv);
-    }
-  }
-}
-
-// === RSSI Visualization ===
-function updateRssi(dbm) {
-  if (!rssiBarEl) return;
-
-  // Clamp to typical WiFi range: -100 (worst) to -30 (best)
-  const clamped = Math.max(-100, Math.min(-30, dbm));
-  const pct = ((clamped + 100) / 70) * 100; // 0-100%
-
-  rssiBarEl.style.width = `${pct}%`;
-  rssiValueEl.textContent = `${Math.round(clamped)} dBm`;
-
-  // Quality label
-  let quality;
-  if (clamped > -50) quality = 'Excellent';
-  else if (clamped > -60) quality = 'Good';
-  else if (clamped > -70) quality = 'Fair';
-  else if (clamped > -80) quality = 'Weak';
-  else quality = 'Poor';
-  rssiQualityEl.textContent = quality;
-
-  // Color the dBm value based on quality
-  if (clamped > -60) rssiValueEl.style.color = 'var(--green-glow)';
-  else if (clamped > -75) rssiValueEl.style.color = 'var(--amber)';
-  else rssiValueEl.style.color = 'var(--red-alert)';
-
-  // Sparkline history
-  rssiHistory.push(clamped);
-  if (rssiHistory.length > RSSI_HISTORY_MAX) rssiHistory.shift();
-  drawRssiSparkline();
-}
-
-function drawRssiSparkline() {
-  if (!rssiSparkCtx || rssiHistory.length < 2) return;
-  const w = rssiSparkCanvas.width;
-  const h = rssiSparkCanvas.height;
-  const ctx = rssiSparkCtx;
-
-  ctx.clearRect(0, 0, w, h);
-
-  // Draw signal strength line
-  const len = rssiHistory.length;
-  const step = w / (RSSI_HISTORY_MAX - 1);
-
-  // Gradient fill under line
-  const grad = ctx.createLinearGradient(0, 0, 0, h);
-  grad.addColorStop(0, 'rgba(0,210,120,0.3)');
-  grad.addColorStop(1, 'rgba(0,210,120,0)');
-
-  ctx.beginPath();
-  for (let i = 0; i < len; i++) {
-    const x = (RSSI_HISTORY_MAX - len + i) * step;
-    const y = h - ((rssiHistory[i] + 100) / 70) * h;
-    if (i === 0) ctx.moveTo(x, y);
-    else ctx.lineTo(x, y);
-  }
-  // Fill area
-  const lastX = (RSSI_HISTORY_MAX - 1) * step;
-  const firstX = (RSSI_HISTORY_MAX - len) * step;
-  ctx.lineTo(lastX, h);
-  ctx.lineTo(firstX, h);
-  ctx.closePath();
-  ctx.fillStyle = grad;
-  ctx.fill();
-
-  // Draw line on top
-  ctx.beginPath();
-  for (let i = 0; i < len; i++) {
-    const x = (RSSI_HISTORY_MAX - len + i) * step;
-    const y = h - ((rssiHistory[i] + 100) / 70) * h;
-    if (i === 0) ctx.moveTo(x, y);
-    else ctx.lineTo(x, y);
-  }
-  ctx.strokeStyle = '#00d878';
-  ctx.lineWidth = 1.5;
-  ctx.stroke();
-
-  // Pulsing dot at latest value
-  const latestX = lastX;
-  const latestY = h - ((rssiHistory[len - 1] + 100) / 70) * h;
-  const pulse = 0.5 + 0.5 * Math.sin(performance.now() / 300);
-  ctx.beginPath();
-  ctx.arc(latestX, latestY, 2 + pulse, 0, Math.PI * 2);
-  ctx.fillStyle = '#00d878';
-  ctx.fill();
-  ctx.beginPath();
-  ctx.arc(latestX, latestY, 4 + pulse * 2, 0, Math.PI * 2);
-  ctx.strokeStyle = `rgba(0,216,120,${0.3 + pulse * 0.3})`;
-  ctx.lineWidth = 1;
-  ctx.stroke();
 }

 // Boot
@@ -9,35 +9,24 @@
 * When person exits frame, CSI data continues tracking (through-wall mode).
 */

-// Extended keypoint definitions: 17 COCO + 9 hand/fingertip approximations = 26 total
+// COCO keypoint definitions
 export const 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',
-  // Extended: hand keypoints (17-25)
-  'left_thumb', 'left_index', 'left_pinky',       // 17, 18, 19
-  'right_thumb', 'right_index', 'right_pinky',    // 20, 21, 22
-  'left_foot_index', 'right_foot_index',           // 23, 24 (toe tips)
-  'neck',                                          // 25 (mid-shoulder)
+  'left_knee', 'right_knee', 'left_ankle', 'right_ankle'
 ];

 // Skeleton connections (pairs of keypoint indices)
 export const SKELETON_CONNECTIONS = [
  [0, 1], [0, 2], [1, 3], [2, 4],           // Head
-  [0, 25],                                    // Nose → neck
-  [25, 5], [25, 6],                           // Neck → shoulders
+  [5, 6],                                     // Shoulders
  [5, 7], [7, 9],                             // Left arm
  [6, 8], [8, 10],                            // Right arm
  [5, 11], [6, 12],                           // Torso
  [11, 12],                                   // Hips
  [11, 13], [13, 15],                         // Left leg
  [12, 14], [14, 16],                         // Right leg
-  // Hand connections
-  [9, 17], [9, 18], [9, 19],                 // Left wrist → fingers
-  [10, 20], [10, 21], [10, 22],              // Right wrist → fingers
-  // Foot connections
-  [15, 23], [16, 24],                         // Ankles → toes
 ];

 // Standard body proportions (relative to body height)
@@ -52,19 +41,13 @@ const PROPORTIONS = {
  kneeToAnkle: 0.24,
  eyeSpacing: 0.04,
  earSpacing: 0.07,
-  // Hand proportions
-  wristToFinger: 0.09,
-  fingerSpread: 0.04,
-  thumbAngle: 0.6,    // radians from wrist-elbow axis
-  // Foot proportions
-  ankleToToe: 0.06,
 };

 export class PoseDecoder {
  constructor(embeddingDim = 128) {
    this.embeddingDim = embeddingDim;
    this.smoothedKeypoints = null;
-    this.smoothingFactor = 0.25; // Low = responsive to real movement
+    this.smoothingFactor = 0.45; // Lower = more responsive to movement
    this._time = 0;

    // Through-wall tracking state
@@ -73,53 +56,12 @@ export class PoseDecoder {
    this._ghostConfidence = 0;
    this._ghostVelocity = { x: 0, y: 0 };

-    // Zone centroid tracking (normalized 0-1 positions)
-    this._headCx = 0.5;
-    this._headCy = 0.15;
-    this._leftArmCx = 0.3;
-    this._leftArmCy = 0.35;
-    this._rightArmCx = 0.7;
-    this._rightArmCy = 0.35;
-    this._leftLegCx = 0.4;
-    this._leftLegCy = 0.8;
-    this._rightLegCx = 0.6;
-    this._rightLegCy = 0.8;
-    this._torsoCx = 0.5;
-    this._torsoCy = 0.45;
-
-    // RuVector embedding → joint mapping
-    // Each joint gets 2 consecutive embedding dimensions (dx, dy offset)
-    // and 1 dimension for confidence modulation. 26 joints × 3 = 78 dims used from 128.
-    // Remaining 50 dims encode global pose features (body scale, rotation, lean).
-    this._jointEmbMap = this._buildJointEmbeddingMap(embeddingDim);
-
-    // Attention contribution tracking (for UI overlay)
-    this.attentionStats = { energy: 0, maxDim: 0, refinementMag: 0 };
-  }
-
-  /**
-   * Build the mapping from embedding dimensions to joint refinement signals.
-   * This maps the RuVector attention output to anatomically meaningful joint offsets.
-   */
-  _buildJointEmbeddingMap(dim) {
-    const map = [];
-    // 26 joints × 3 dims each (dx, dy, confidence_mod) = 78 dims
-    for (let j = 0; j < 26; j++) {
-      const base = j * 3;
-      if (base + 2 < dim) {
-        map.push({ dxDim: base, dyDim: base + 1, confDim: base + 2 });
-      } else {
-        map.push({ dxDim: j % dim, dyDim: (j + 1) % dim, confDim: (j + 2) % dim });
-      }
-    }
-    // Global pose features from dims 78-127
-    return {
-      joints: map,
-      scaleDim: Math.min(78, dim - 1),       // body scale factor
-      rotDim: Math.min(79, dim - 1),          // body rotation
-      leanXDim: Math.min(80, dim - 1),        // lateral lean
-      leanYDim: Math.min(81, dim - 1),        // forward/back lean
-    };
+    // Arm tracking history (smoothed positions)
+    this._leftArmY = 0.5;
+    this._rightArmY = 0.5;
+    this._leftArmX = 0;
+    this._rightArmX = 0;
+    this._headOffsetX = 0;
  }

  /**
@@ -183,129 +125,71 @@ export class PoseDecoder {

  /**
   * Track body parts from the motion grid.
-   * Finds the centroid of motion in each body zone and positions joints there.
+   * The grid tells us WHERE motion is happening → we map that to joint positions.
   */
  _trackFromMotionGrid(region, embedding, elapsed) {
    const grid = region.motionGrid;
    const cols = region.gridCols || 10;
    const rows = region.gridRows || 8;

-    // Body bounding box (in normalized 0-1 coords)
-    const bx = region.x, by = region.y, bw = region.w, bh = region.h;
-    const cx = bx + bw / 2;
-    const cy = by + bh / 2;
-    const bodyH = Math.max(bh, 0.3);
-    const bodyW = Math.max(bw, 0.15);
+    // Body bounding box
+    const cx = region.x + region.w / 2;
+    const cy = region.y + region.h / 2;
+    const bodyH = Math.max(region.h, 0.3);
+    const bodyW = Math.max(region.w, 0.15);

-    // Find motion centroids per body zone from the grid
+    // Analyze the motion grid to find arm positions
+    // Divide body into zones: head (top 20%), arms (top 60% sides), torso (center), legs (bottom 40%)
    if (grid) {
-      const zones = this._findZoneCentroids(grid, cols, rows, bx, by, bw, bh);
-      // Smooth with low alpha for responsiveness
-      const a = 0.3; // 30% old, 70% new → responsive
-      this._headCx    = a * this._headCx    + (1 - a) * zones.head.x;
-      this._headCy    = a * this._headCy    + (1 - a) * zones.head.y;
-      this._leftArmCx = a * this._leftArmCx + (1 - a) * zones.leftArm.x;
-      this._leftArmCy = a * this._leftArmCy + (1 - a) * zones.leftArm.y;
-      this._rightArmCx= a * this._rightArmCx+ (1 - a) * zones.rightArm.x;
-      this._rightArmCy= a * this._rightArmCy+ (1 - a) * zones.rightArm.y;
-      this._leftLegCx = a * this._leftLegCx + (1 - a) * zones.leftLeg.x;
-      this._leftLegCy = a * this._leftLegCy + (1 - a) * zones.leftLeg.y;
-      this._rightLegCx= a * this._rightLegCx+ (1 - a) * zones.rightLeg.x;
-      this._rightLegCy= a * this._rightLegCy+ (1 - a) * zones.rightLeg.y;
-      this._torsoCx   = a * this._torsoCx   + (1 - a) * zones.torso.x;
-      this._torsoCy   = a * this._torsoCy   + (1 - a) * zones.torso.y;
+      const armAnalysis = this._analyzeArmMotion(grid, cols, rows, region);
+      // Smooth arm tracking
+      this._leftArmY = 0.6 * this._leftArmY + 0.4 * armAnalysis.leftArmHeight;
+      this._rightArmY = 0.6 * this._rightArmY + 0.4 * armAnalysis.rightArmHeight;
+      this._leftArmX = 0.6 * this._leftArmX + 0.4 * armAnalysis.leftArmSpread;
+      this._rightArmX = 0.6 * this._rightArmX + 0.4 * armAnalysis.rightArmSpread;
+      this._headOffsetX = 0.7 * this._headOffsetX + 0.3 * armAnalysis.headOffsetX;
    }

    const P = PROPORTIONS;
+    const halfW = P.shoulderWidth * bodyH / 2;
+    const hipHalfW = P.hipWidth * bodyH / 2;

    // Breathing (subtle)
    const breathe = Math.sin(elapsed * 1.5) * 0.002;

-    // === Position joints using tracked centroids ===
+    // Core body positions from detection center
+    const hipY = cy + bodyH * 0.15;
+    const shoulderY = hipY - P.shoulderToHip * bodyH + breathe;
+    const headY = shoulderY - P.headToShoulder * bodyH;
+    const kneeY = hipY + P.hipToKnee * bodyH;
+    const ankleY = kneeY + P.kneeToAnkle * bodyH;

-    // HEAD: tracked centroid (top zone)
-    const headX = this._headCx;
-    const headY = this._headCy;
+    // HEAD follows motion centroid
+    const headX = cx + this._headOffsetX * bodyW * 0.3;

-    // TORSO center drives shoulder/hip
-    const torsoX = this._torsoCx;
-    const shoulderY = this._torsoCy - bodyH * 0.08 + breathe;
-    const halfW = P.shoulderWidth * bodyH / 2;
-    const hipHalfW = P.hipWidth * bodyH / 2;
-    const hipY = shoulderY + P.shoulderToHip * bodyH;
+    // ARM POSITIONS driven by motion grid analysis
+    // leftArmY: 0 = arm down at side, 1 = arm fully raised
+    // leftArmSpread: how far out the arm extends
+    const leftArmRaise = this._leftArmY;  // 0-1
+    const rightArmRaise = this._rightArmY;
+    const leftSpread = 0.02 + this._leftArmX * 0.12;
+    const rightSpread = 0.02 + this._rightArmX * 0.12;

-    // ARMS: elbow + wrist driven toward arm zone centroids
-    // Left arm: shoulder is fixed, elbow/wrist pulled toward left arm centroid
-    const lShX = torsoX - halfW;
-    const lShY = shoulderY;
-    // Vector from shoulder toward arm centroid
-    const lArmDx = this._leftArmCx - lShX;
-    const lArmDy = this._leftArmCy - lShY;
-    const lArmDist = Math.sqrt(lArmDx * lArmDx + lArmDy * lArmDy) || 0.01;
-    const lArmNx = lArmDx / lArmDist;
-    const lArmNy = lArmDy / lArmDist;
-    // Elbow at shoulderToElbow distance along that direction
-    const elbowLen = P.shoulderToElbow * bodyH;
-    const lElbowX = lShX + lArmNx * elbowLen;
-    const lElbowY = lShY + lArmNy * elbowLen;
-    // Wrist continues further
-    const wristLen = P.elbowToWrist * bodyH;
-    const lWristX = lElbowX + lArmNx * wristLen;
-    const lWristY = lElbowY + lArmNy * wristLen;
+    // Elbow: interpolate between "at side" and "raised"
+    const lElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - leftArmRaise * 0.9);
+    const rElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - rightArmRaise * 0.9);
+    const lElbowX = cx - halfW - leftSpread;
+    const rElbowX = cx + halfW + rightSpread;

-    // Right arm: same approach
-    const rShX = torsoX + halfW;
-    const rShY = shoulderY;
-    const rArmDx = this._rightArmCx - rShX;
-    const rArmDy = this._rightArmCy - rShY;
-    const rArmDist = Math.sqrt(rArmDx * rArmDx + rArmDy * rArmDy) || 0.01;
-    const rArmNx = rArmDx / rArmDist;
-    const rArmNy = rArmDy / rArmDist;
-    const rElbowX = rShX + rArmNx * elbowLen;
-    const rElbowY = rShY + rArmNy * elbowLen;
-    const rWristX = rElbowX + rArmNx * wristLen;
-    const rWristY = rElbowY + rArmNy * wristLen;
+    // Wrist: extends further when raised
+    const lWristY = lElbowY + P.elbowToWrist * bodyH * (1 - leftArmRaise * 1.1);
+    const rWristY = rElbowY + P.elbowToWrist * bodyH * (1 - rightArmRaise * 1.1);
+    const lWristX = lElbowX - leftSpread * 0.6;
+    const rWristX = rElbowX + rightSpread * 0.6;

-    // LEGS: knees/ankles pulled toward leg zone centroids
-    const lHipX = torsoX - hipHalfW;
-    const rHipX = torsoX + hipHalfW;
-    const lLegDx = this._leftLegCx - lHipX;
-    const lLegDy = Math.max(0.05, this._leftLegCy - hipY); // always downward
-    const lLegDist = Math.sqrt(lLegDx * lLegDx + lLegDy * lLegDy) || 0.01;
-    const lLegNx = lLegDx / lLegDist;
-    const lLegNy = lLegDy / lLegDist;
-    const kneeLen = P.hipToKnee * bodyH;
-    const ankleLen = P.kneeToAnkle * bodyH;
-    const lKneeX = lHipX + lLegNx * kneeLen;
-    const lKneeY = hipY + lLegNy * kneeLen;
-    const lAnkleX = lKneeX + lLegNx * ankleLen;
-    const lAnkleY = lKneeY + lLegNy * ankleLen;
-
-    const rLegDx = this._rightLegCx - rHipX;
-    const rLegDy = Math.max(0.05, this._rightLegCy - hipY);
-    const rLegDist = Math.sqrt(rLegDx * rLegDx + rLegDy * rLegDy) || 0.01;
-    const rLegNx = rLegDx / rLegDist;
-    const rLegNy = rLegDy / rLegDist;
-    const rKneeX = rHipX + rLegNx * kneeLen;
-    const rKneeY = hipY + rLegNy * kneeLen;
-    const rAnkleX = rKneeX + rLegNx * ankleLen;
-    const rAnkleY = rKneeY + rLegNy * ankleLen;
-
-    // Arm raise amount (for hand openness)
-    const leftArmRaise = Math.max(0, Math.min(1, (shoulderY - this._leftArmCy) / (bodyH * 0.3)));
-    const rightArmRaise = Math.max(0, Math.min(1, (shoulderY - this._rightArmCy) / (bodyH * 0.3)));
-
-    // Compute hand finger positions from wrist-elbow axis
-    const lHandAngle = Math.atan2(lWristY - lElbowY, lWristX - lElbowX);
-    const rHandAngle = Math.atan2(rWristY - rElbowY, rWristX - rElbowX);
-    const fingerLen = P.wristToFinger * bodyH;
-    const fingerSpr = P.fingerSpread * bodyH;
-
-    // Hand openness driven by arm raise + arm lateral spread
-    const lArmSpread = Math.abs(this._leftArmCx - (bx + bw * 0.3)) / (bw * 0.3);
-    const rArmSpread = Math.abs(this._rightArmCx - (bx + bw * 0.7)) / (bw * 0.3);
-    const lHandOpen = Math.min(1, leftArmRaise * 0.5 + lArmSpread * 0.5);
-    const rHandOpen = Math.min(1, rightArmRaise * 0.5 + rArmSpread * 0.5);
+    // Leg motion from lower grid cells
+    const legMotion = grid ? this._analyzeLegMotion(grid, cols, rows) : { left: 0, right: 0 };
+    const legSwing = 0.015;

    const keypoints = [
      // 0: nose
@@ -319,9 +203,9 @@ export class PoseDecoder {
      // 4: right_ear
      { x: headX + P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
      // 5: left_shoulder
-      { x: lShX, y: lShY, confidence: 0.94 },
+      { x: cx - halfW, y: shoulderY, confidence: 0.94 },
      // 6: right_shoulder
-      { x: rShX, y: rShY, confidence: 0.94 },
+      { x: cx + halfW, y: shoulderY, confidence: 0.94 },
      // 7: left_elbow
      { x: lElbowX, y: lElbowY, confidence: 0.87 },
      // 8: right_elbow
@@ -331,179 +215,115 @@ export class PoseDecoder {
      // 10: right_wrist
      { x: rWristX, y: rWristY, confidence: 0.82 },
      // 11: left_hip
-      { x: lHipX, y: hipY, confidence: 0.91 },
+      { x: cx - hipHalfW, y: hipY, confidence: 0.91 },
      // 12: right_hip
-      { x: rHipX, y: hipY, confidence: 0.91 },
+      { x: cx + hipHalfW, y: hipY, confidence: 0.91 },
      // 13: left_knee
-      { x: lKneeX, y: lKneeY, confidence: 0.88 },
+      { x: cx - hipHalfW + legMotion.left * legSwing, y: kneeY, confidence: 0.88 },
      // 14: right_knee
-      { x: rKneeX, y: rKneeY, confidence: 0.88 },
+      { x: cx + hipHalfW + legMotion.right * legSwing, y: kneeY, confidence: 0.88 },
      // 15: left_ankle
-      { x: lAnkleX, y: lAnkleY, confidence: 0.83 },
+      { x: cx - hipHalfW + legMotion.left * legSwing * 1.3, y: ankleY, confidence: 0.83 },
      // 16: right_ankle
-      { x: rAnkleX, y: rAnkleY, confidence: 0.83 },
-
-      // === Extended keypoints (17-25) ===
-
-      // 17: left_thumb — offset at thumb angle from wrist-elbow axis
-      { x: lWristX + fingerLen * Math.cos(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4),
-        y: lWristY + fingerLen * Math.sin(lHandAngle + P.thumbAngle) * (0.6 + lHandOpen * 0.4),
-        confidence: 0.68 * (0.5 + lHandOpen * 0.5) },
-      // 18: left_index — extends along wrist-elbow axis
-      { x: lWristX + fingerLen * Math.cos(lHandAngle) + fingerSpr * lHandOpen * Math.cos(lHandAngle + 0.3),
-        y: lWristY + fingerLen * Math.sin(lHandAngle) + fingerSpr * lHandOpen * Math.sin(lHandAngle + 0.3),
-        confidence: 0.72 * (0.5 + lHandOpen * 0.5) },
-      // 19: left_pinky — offset opposite thumb
-      { x: lWristX + fingerLen * 0.85 * Math.cos(lHandAngle - P.thumbAngle * 0.7),
-        y: lWristY + fingerLen * 0.85 * Math.sin(lHandAngle - P.thumbAngle * 0.7),
-        confidence: 0.60 * (0.5 + lHandOpen * 0.5) },
-
-      // 20: right_thumb
-      { x: rWristX + fingerLen * Math.cos(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4),
-        y: rWristY + fingerLen * Math.sin(rHandAngle - P.thumbAngle) * (0.6 + rHandOpen * 0.4),
-        confidence: 0.68 * (0.5 + rHandOpen * 0.5) },
-      // 21: right_index
-      { x: rWristX + fingerLen * Math.cos(rHandAngle) + fingerSpr * rHandOpen * Math.cos(rHandAngle - 0.3),
-        y: rWristY + fingerLen * Math.sin(rHandAngle) + fingerSpr * rHandOpen * Math.sin(rHandAngle - 0.3),
-        confidence: 0.72 * (0.5 + rHandOpen * 0.5) },
-      // 22: right_pinky
-      { x: rWristX + fingerLen * 0.85 * Math.cos(rHandAngle + P.thumbAngle * 0.7),
-        y: rWristY + fingerLen * 0.85 * Math.sin(rHandAngle + P.thumbAngle * 0.7),
-        confidence: 0.60 * (0.5 + rHandOpen * 0.5) },
-
-      // 23: left_foot_index (toe tip) — extends forward from ankle
-      { x: lAnkleX + P.ankleToToe * bodyH * 0.5,
-        y: lAnkleY + P.ankleToToe * bodyH * 0.3,
-        confidence: 0.65 },
-      // 24: right_foot_index
-      { x: rAnkleX + P.ankleToToe * bodyH * 0.5,
-        y: rAnkleY + P.ankleToToe * bodyH * 0.3,
-        confidence: 0.65 },
-
-      // 25: neck (midpoint between shoulders, slightly above)
-      { x: (lShX + rShX) / 2, y: shoulderY - P.headToShoulder * bodyH * 0.35, confidence: 0.93 },
+      { x: cx + hipHalfW + legMotion.right * legSwing * 1.3, y: ankleY, confidence: 0.83 },
    ];

    for (let i = 0; i < keypoints.length; i++) {
      keypoints[i].name = KEYPOINT_NAMES[i];
    }

-    // === RuVector Attention Embedding Refinement ===
-    // Compute attention stats for the UI pipeline display, but only apply
-    // positional refinement when a trained model is loaded (random-weight
-    // embeddings carry no meaningful spatial signal and distort the skeleton).
-    if (embedding && embedding.length >= 26 * 3) {
-      this._computeEmbeddingStats(keypoints, embedding, bodyH);
-    }
-
    return keypoints;
  }

  /**
-   * Apply RuVector attention embedding to refine joint positions and confidence.
-   *
-   * The 128-dim fused embedding is decoded as:
-   * - Dims 0-77:  Per-joint (dx, dy, confidence_mod) × 26 joints
-   * - Dims 78-81: Global pose parameters (scale, rotation, lean)
-   * - Dims 82-127: Reserved for cross-modal fusion features
-   *
-   * The attention mechanism determines HOW MUCH each spatial region contributes
-   * to each joint's refinement. Multi-Head captures global relationships,
-   * Hyperbolic captures hierarchical (torso→limb→hand) dependencies,
-   * MoE routes different body regions to specialized experts,
-   * Linear provides fast extremity refinement, Local-Global balances detail/context.
+   * Analyze the motion grid to determine arm positions.
+   * Left side of grid = left side of body, etc.
   */
-  /**
-   * Compute embedding statistics for UI display without modifying joint positions.
-   * The 6-stage attention pipeline stats are shown in the RuVector panel.
-   * Position refinement is disabled until a trained model replaces random weights.
-   */
-  _computeEmbeddingStats(keypoints, emb, bodyH) {
-    const map = this._jointEmbMap;
-    const tc = (v) => Math.tanh(Number(v) || 0);
+  _analyzeArmMotion(grid, cols, rows, region) {
+    // Body center column
+    const centerCol = Math.floor(cols / 2);

-    // Embedding energy (L2 norm of the used dims)
-    let energy = 0;
-    for (let i = 0; i < Math.min(emb.length, 82); i++) {
-      energy += emb[i] * emb[i];
-    }
-    energy = Math.sqrt(energy);
+    // Upper body rows (top 60% of detected region)
+    const upperEnd = Math.floor(rows * 0.6);

-    // Simulated per-joint refinement magnitude (what WOULD be applied)
-    const scale = bodyH * 0.015;
-    let totalRefinement = 0;
-    let maxDimVal = 0;
+    // Compute motion intensity for left vs right, at different heights
+    let leftUpperMotion = 0, leftMidMotion = 0;
+    let rightUpperMotion = 0, rightMidMotion = 0;
+    let leftCount = 0, rightCount = 0;
+    let headMotionX = 0, headMotionWeight = 0;

-    for (let j = 0; j < Math.min(keypoints.length, 26); j++) {
-      const jmap = map.joints[j];
-      if (!jmap) continue;
-      const dx = tc(emb[jmap.dxDim]) * scale;
-      const dy = tc(emb[jmap.dyDim]) * scale;
-      totalRefinement += Math.sqrt(dx * dx + dy * dy);
-      maxDimVal = Math.max(maxDimVal, Math.abs(tc(emb[jmap.dxDim])), Math.abs(tc(emb[jmap.dyDim])));
-    }
+    for (let r = 0; r < upperEnd; r++) {
+      const heightWeight = 1.0 - (r / upperEnd) * 0.3; // Upper rows weighted more

-    this.attentionStats.energy = energy;
-    this.attentionStats.maxDim = maxDimVal;
-    this.attentionStats.refinementMag = totalRefinement / 26;
-  }
-
-  /**
-   * Find weighted motion centroids for each body zone.
-   * Divides the bounding box into 6 zones: head, left arm, right arm, torso, left leg, right leg.
-   * Returns the (x,y) centroid of motion intensity for each zone.
-   */
-  _findZoneCentroids(grid, cols, rows, bx, by, bw, bh) {
-    // Zone definitions (in grid-relative fractions)
-    const zones = {
-      head:     { rMin: 0,    rMax: 0.2,  cMin: 0.25, cMax: 0.75, wx: 0, wy: 0, wt: 0 },
-      leftArm:  { rMin: 0.1,  rMax: 0.6,  cMin: 0,    cMax: 0.35, wx: 0, wy: 0, wt: 0 },
-      rightArm: { rMin: 0.1,  rMax: 0.6,  cMin: 0.65, cMax: 1.0,  wx: 0, wy: 0, wt: 0 },
-      torso:    { rMin: 0.15, rMax: 0.55, cMin: 0.3,  cMax: 0.7,  wx: 0, wy: 0, wt: 0 },
-      leftLeg:  { rMin: 0.5,  rMax: 1.0,  cMin: 0.1,  cMax: 0.5,  wx: 0, wy: 0, wt: 0 },
-      rightLeg: { rMin: 0.5,  rMax: 1.0,  cMin: 0.5,  cMax: 0.9,  wx: 0, wy: 0, wt: 0 },
-    };
-
-    // Accumulate weighted centroids per zone
-    for (let r = 0; r < rows; r++) {
-      const ry = r / rows; // 0-1 within grid
-      for (let c = 0; c < cols; c++) {
-        const cx_g = c / cols; // 0-1 within grid
-        const val = grid[r][c];
-        if (val < 0.005) continue; // skip near-zero motion
-
-        // Map grid position to body-space coordinates (0-1)
-        const worldX = bx + cx_g * bw;
-        const worldY = by + ry * bh;
-
-        // Assign to matching zones (a cell can contribute to multiple overlapping zones)
-        for (const z of Object.values(zones)) {
-          if (ry >= z.rMin && ry < z.rMax && cx_g >= z.cMin && cx_g < z.cMax) {
-            z.wx += worldX * val;
-            z.wy += worldY * val;
-            z.wt += val;
-          }
+      // Head zone: top 25%, center 40% of width
+      if (r < Math.floor(rows * 0.25)) {
+        const headLeft = Math.floor(cols * 0.3);
+        const headRight = Math.floor(cols * 0.7);
+        for (let c = headLeft; c <= headRight; c++) {
+          const val = grid[r][c];
+          headMotionX += (c / cols - 0.5) * val;
+          headMotionWeight += val;
        }
      }
+
+      // Left arm zone: left 40% of grid
+      for (let c = 0; c < Math.floor(cols * 0.4); c++) {
+        const val = grid[r][c];
+        if (r < rows * 0.3) leftUpperMotion += val * heightWeight;
+        else leftMidMotion += val * heightWeight;
+        leftCount++;
+      }
+
+      // Right arm zone: right 40% of grid
+      for (let c = Math.floor(cols * 0.6); c < cols; c++) {
+        const val = grid[r][c];
+        if (r < rows * 0.3) rightUpperMotion += val * heightWeight;
+        else rightMidMotion += val * heightWeight;
+        rightCount++;
+      }
    }

-    // Compute centroids with fallback defaults
-    const centroid = (z, defX, defY) => ({
-      x: z.wt > 0.01 ? z.wx / z.wt : defX,
-      y: z.wt > 0.01 ? z.wy / z.wt : defY,
-      weight: z.wt
-    });
+    // Normalize
+    const leftTotal = leftUpperMotion + leftMidMotion;
+    const rightTotal = rightUpperMotion + rightMidMotion;
+    const maxMotion = 0.15; // Calibration threshold

-    const midX = bx + bw / 2;
-    const midY = by + bh / 2;
+    // Arm height: 0 = at side, 1 = raised
+    // High motion in upper-left → left arm is raised
+    const leftArmHeight = Math.min(1, (leftUpperMotion / maxMotion) * 2);
+    const rightArmHeight = Math.min(1, (rightUpperMotion / maxMotion) * 2);

+    // Arm spread: how far out from body
+    const leftArmSpread = Math.min(1, leftTotal / maxMotion);
+    const rightArmSpread = Math.min(1, rightTotal / maxMotion);
+
+    // Head offset
+    const headOffsetX = headMotionWeight > 0.01 ? headMotionX / headMotionWeight : 0;
+
+    return { leftArmHeight, rightArmHeight, leftArmSpread, rightArmSpread, headOffsetX };
+  }
+
+  /**
+   * Analyze lower grid for leg motion.
+   */
+  _analyzeLegMotion(grid, cols, rows) {
+    const lowerStart = Math.floor(rows * 0.6);
+    let leftMotion = 0, rightMotion = 0;
+
+    for (let r = lowerStart; r < rows; r++) {
+      for (let c = 0; c < Math.floor(cols / 2); c++) {
+        leftMotion += grid[r][c];
+      }
+      for (let c = Math.floor(cols / 2); c < cols; c++) {
+        rightMotion += grid[r][c];
+      }
+    }
+
+    // Return as -1 to 1 range (asymmetry indicates which leg is moving)
+    const total = leftMotion + rightMotion + 0.001;
    return {
-      head:     centroid(zones.head,     midX,           by + bh * 0.1),
-      leftArm:  centroid(zones.leftArm,  bx + bw * 0.2, midY - bh * 0.05),
-      rightArm: centroid(zones.rightArm, bx + bw * 0.8, midY - bh * 0.05),
-      torso:    centroid(zones.torso,    midX,           midY),
-      leftLeg:  centroid(zones.leftLeg,  bx + bw * 0.35,by + bh * 0.75),
-      rightLeg: centroid(zones.rightLeg, bx + bw * 0.65,by + bh * 0.75),
+      left: (leftMotion - rightMotion) / total,
+      right: (rightMotion - leftMotion) / total
    };
  }

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2025 rUv
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
@@ -1,220 +0,0 @@
-# ruvector-attention-wasm
-
-WebAssembly bindings for the ruvector-attention package, providing high-performance attention mechanisms for browser and Node.js environments.
-
-## Features
-
- **Multiple Attention Mechanisms**:
-  - Scaled Dot-Product Attention
-  - Multi-Head Attention
-  - Hyperbolic Attention (for hierarchical data)
-  - Linear Attention (Performer-style)
-  - Flash Attention (memory-efficient)
-  - Local-Global Attention
-  - Mixture of Experts (MoE) Attention
-  - **CGT Sheaf Attention** (coherence-gated via Prime-Radiant)
-
- **Training Utilities**:
-  - InfoNCE contrastive loss
-  - Adam optimizer
-  - AdamW optimizer (with decoupled weight decay)
-  - Learning rate scheduler (warmup + cosine decay)
-
- **TypeScript Support**: Full type definitions and modern API
-
-## Installation
-
-```bash
-npm install ruvector-attention-wasm
-```
-
-## Usage
-
-### TypeScript/JavaScript
-
-```typescript
-import { initialize, MultiHeadAttention, utils } from 'ruvector-attention-wasm';
-
-// Initialize WASM module
-await initialize();
-
-// Create multi-head attention
-const attention = new MultiHeadAttention({ dim: 64, numHeads: 8 });
-
-// Prepare inputs
-const query = new Float32Array(64);
-const keys = [new Float32Array(64), new Float32Array(64)];
-const values = [new Float32Array(64), new Float32Array(64)];
-
-// Compute attention
-const output = attention.compute(query, keys, values);
-
-// Use utilities
-const similarity = utils.cosineSimilarity(query, keys[0]);
-```
-
-### Advanced Examples
-
-#### Hyperbolic Attention
-
-```typescript
-import { HyperbolicAttention } from 'ruvector-attention-wasm';
-
-const hyperbolic = new HyperbolicAttention({
-  dim: 128,
-  curvature: 1.0
-});
-
-const output = hyperbolic.compute(query, keys, values);
-```
-
-#### MoE Attention with Expert Stats
-
-```typescript
-import { MoEAttention } from 'ruvector-attention-wasm';
-
-const moe = new MoEAttention({
-  dim: 64,
-  numExperts: 4,
-  topK: 2
-});
-
-const output = moe.compute(query, keys, values);
-
-// Get expert utilization
-const stats = moe.getExpertStats();
-console.log('Load balance:', stats.loadBalance);
-```
-
-#### Training with InfoNCE Loss
-
-```typescript
-import { InfoNCELoss, Adam } from 'ruvector-attention-wasm';
-
-const loss = new InfoNCELoss(0.07);
-const optimizer = new Adam(paramCount, {
-  learningRate: 0.001,
-  beta1: 0.9,
-  beta2: 0.999,
-});
-
-// Training loop
-const lossValue = loss.compute(anchor, positive, negatives);
-optimizer.step(params, gradients);
-```
-
-#### Learning Rate Scheduling
-
-```typescript
-import { LRScheduler, AdamW } from 'ruvector-attention-wasm';
-
-const scheduler = new LRScheduler({
-  initialLR: 0.001,
-  warmupSteps: 1000,
-  totalSteps: 10000,
-});
-
-const optimizer = new AdamW(paramCount, {
-  learningRate: scheduler.getLR(),
-  weightDecay: 0.01,
-});
-
-// Training loop
-for (let step = 0; step < 10000; step++) {
-  optimizer.learningRate = scheduler.getLR();
-  optimizer.step(params, gradients);
-  scheduler.step();
-}
-```
-
-## Building from Source
-
-### Prerequisites
-
- Rust 1.70+
- wasm-pack
-
-### Build Commands
-
-```bash
-# Build for web (ES modules)
-wasm-pack build --target web --out-dir pkg
-
-# Build for Node.js
-wasm-pack build --target nodejs --out-dir pkg-node
-
-# Build for bundlers (webpack, vite, etc.)
-wasm-pack build --target bundler --out-dir pkg-bundler
-
-# Run tests
-wasm-pack test --headless --firefox
-```
-
-## API Reference
-
-### Attention Mechanisms
-
- `MultiHeadAttention` - Standard multi-head attention
- `HyperbolicAttention` - Attention in hyperbolic space
- `LinearAttention` - Linear complexity attention (Performer)
- `FlashAttention` - Memory-efficient attention
- `LocalGlobalAttention` - Combined local and global attention
- `MoEAttention` - Mixture of Experts attention
- `CGTSheafAttention` - Coherence-gated via Prime-Radiant energy
- `scaledDotAttention()` - Functional API for basic attention
-
-### CGT Sheaf Attention (Prime-Radiant Integration)
-
-The CGT (Coherence-Gated Transformer) Sheaf Attention mechanism uses Prime-Radiant's sheaf Laplacian energy to gate attention based on mathematical consistency:
-
-```typescript
-import { CGTSheafAttention } from 'ruvector-attention-wasm';
-
-const cgtAttention = new CGTSheafAttention({
-  dim: 128,
-  numHeads: 8,
-  coherenceThreshold: 0.3,  // Block if energy > threshold
-});
-
-// Attention is gated by coherence energy
-const result = cgtAttention.compute(query, keys, values);
-console.log('Coherence energy:', result.energy);
-console.log('Is coherent:', result.isCoherent);
-```
-
-**Key features:**
- Energy-weighted attention: Lower coherence energy → higher attention
- Automatic hallucination detection via residual analysis
- GPU-accelerated with wgpu WGSL shaders (vec4 optimized)
- SIMD fallback (AVX-512/AVX2/NEON)
-
-### Training
-
- `InfoNCELoss` - Contrastive loss function
- `Adam` - Adam optimizer
- `AdamW` - AdamW optimizer with weight decay
- `LRScheduler` - Learning rate scheduler
-
-### Utilities
-
- `utils.cosineSimilarity()` - Cosine similarity between vectors
- `utils.l2Norm()` - L2 norm of a vector
- `utils.normalize()` - Normalize vector to unit length
- `utils.softmax()` - Apply softmax transformation
- `utils.attentionWeights()` - Compute attention weights from scores
- `utils.batchNormalize()` - Batch normalization
- `utils.randomOrthogonalMatrix()` - Generate random orthogonal matrix
- `utils.pairwiseDistances()` - Compute pairwise distances
-
-## Performance
-
-The WASM bindings provide near-native performance for attention computations:
-
- Optimized with `opt-level = "s"` and LTO
- SIMD acceleration where available
- Efficient memory management
- Zero-copy data transfer where possible
-
-## License
-
-MIT OR Apache-2.0
@@ -1,28 +0,0 @@
-{
-  "name": "ruvector-attention-wasm",
-  "collaborators": [
-    "Ruvector Team"
-  ],
-  "description": "High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs",
-  "version": "2.0.5",
-  "license": "MIT",
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/ruvnet/ruvector"
-  },
-  "files": [
-    "ruvector_attention_wasm_bg.wasm",
-    "ruvector_attention_wasm.js",
-    "ruvector_attention_wasm.d.ts"
-  ],
-  "main": "ruvector_attention_wasm.js",
-  "homepage": "https://ruv.io/ruvector",
-  "types": "ruvector_attention_wasm.d.ts",
-  "keywords": [
-    "wasm",
-    "attention",
-    "transformer",
-    "flash-attention",
-    "llm"
-  ]
-}
@@ -1,642 +0,0 @@
-/**
- * Browser ESM wrapper for ruvector-attention-wasm v2.0.5
- *
- * The upstream pkg/ was built with wasm-pack --target nodejs (CJS + fs.readFileSync).
- * This wrapper loads the same WASM binary via fetch() for browser use.
- *
- * Usage:
- *   import initWasm, { WasmMultiHeadAttention, ... } from './ruvector_attention_browser.js';
- *   await initWasm();
- *   const attn = new WasmMultiHeadAttention(dim, heads);
- */
-
-let _wasm;
-let _initialized = false;
-
-// The entire CJS module runs inside this IIFE to avoid polluting global scope.
-// We capture all exports in _mod.
-const _mod = {};
-
-(function(exports, wasm_getter) {
-
-// ── wasm-bindgen heap management ──────────────────────────────────
-const heap = new Array(128).fill(undefined);
-heap.push(undefined, null, true, false);
-let heap_next = heap.length;
-
-function addHeapObject(obj) {
-  if (heap_next === heap.length) heap.push(heap.length + 1);
-  const idx = heap_next;
-  heap_next = heap[idx];
-  heap[idx] = obj;
-  return idx;
-}
-function getObject(idx) { return heap[idx]; }
-function dropObject(idx) {
-  if (idx < 132) return;
-  heap[idx] = heap_next;
-  heap_next = idx;
-}
-function takeObject(idx) {
-  const ret = getObject(idx);
-  dropObject(idx);
-  return ret;
-}
-function isLikeNone(x) { return x === undefined || x === null; }
-
-// ── Memory views ──────────────────────────────────────────────────
-let cachedDataViewMemory0 = null;
-let cachedUint8ArrayMemory0 = null;
-let cachedFloat32ArrayMemory0 = null;
-
-function wasm() { return wasm_getter(); }
-
-function getDataViewMemory0() {
-  if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer !== wasm().memory.buffer)
-    cachedDataViewMemory0 = new DataView(wasm().memory.buffer);
-  return cachedDataViewMemory0;
-}
-function getUint8ArrayMemory0() {
-  if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.buffer !== wasm().memory.buffer)
-    cachedUint8ArrayMemory0 = new Uint8Array(wasm().memory.buffer);
-  return cachedUint8ArrayMemory0;
-}
-function getFloat32ArrayMemory0() {
-  if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.buffer !== wasm().memory.buffer)
-    cachedFloat32ArrayMemory0 = new Float32Array(wasm().memory.buffer);
-  return cachedFloat32ArrayMemory0;
-}
-function getArrayF32FromWasm0(ptr, len) {
-  ptr = ptr >>> 0;
-  return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len);
-}
-function getArrayU8FromWasm0(ptr, len) {
-  ptr = ptr >>> 0;
-  return getUint8ArrayMemory0().subarray(ptr, ptr + len);
-}
-
-let WASM_VECTOR_LEN = 0;
-
-function passArrayF32ToWasm0(arg, malloc) {
-  const ptr = malloc(arg.length * 4, 4) >>> 0;
-  getFloat32ArrayMemory0().set(arg, ptr / 4);
-  WASM_VECTOR_LEN = arg.length;
-  return ptr;
-}
-
-const cachedTextEncoder = new TextEncoder();
-const cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
-cachedTextDecoder.decode();
-
-function getStringFromWasm0(ptr, len) {
-  ptr = ptr >>> 0;
-  return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len));
-}
-
-function passStringToWasm0(arg, malloc, realloc) {
-  const buf = cachedTextEncoder.encode(arg);
-  const ptr = malloc(buf.length, 1) >>> 0;
-  getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf);
-  WASM_VECTOR_LEN = buf.length;
-  return ptr;
-}
-
-function debugString(val) {
-  const type = typeof val;
-  if (type == 'number' || type == 'boolean' || val == null) return `${val}`;
-  if (type == 'string') return `"${val}"`;
-  if (type == 'symbol') return val.description ? `Symbol(${val.description})` : 'Symbol';
-  if (type == 'function') return 'Function';
-  if (Array.isArray(val)) return `[${val.map(debugString).join(', ')}]`;
-  try {
-    const keys = Object.keys(val);
-    return `{${keys.map(k => `${k}: ${debugString(val[k])}`).join(', ')}}`;
-  } catch (_) { return Object.prototype.toString.call(val); }
-}
-
-function handleError(f, args) {
-  try { return f.apply(this, args); }
-  catch (e) { wasm().__wbindgen_export3(addHeapObject(e)); }
-}
-
-// ── FinalizationRegistry ──────────────────────────────────────────
-const FR = typeof FinalizationRegistry !== 'undefined'
-  ? FinalizationRegistry
-  : class { register() {} unregister() {} };
-
-const WasmMultiHeadAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmultiheadattention_free(ptr >>> 0, 1));
-const WasmFlashAttentionFinalization = new FR(ptr => wasm().__wbg_wasmflashattention_free(ptr >>> 0, 1));
-const WasmHyperbolicAttentionFinalization = new FR(ptr => wasm().__wbg_wasmhyperbolicattention_free(ptr >>> 0, 1));
-const WasmMoEAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmoeattention_free(ptr >>> 0, 1));
-const WasmLinearAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlinearattention_free(ptr >>> 0, 1));
-const WasmLocalGlobalAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlocalglobalattention_free(ptr >>> 0, 1));
-
-// ── Classes ───────────────────────────────────────────────────────
-
-class WasmMultiHeadAttention {
-  constructor(dim, num_heads) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      wasm().wasmmultiheadattention_new(retptr, dim, num_heads);
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      if (r2) throw takeObject(r1);
-      this.__wbg_ptr = r0 >>> 0;
-      WasmMultiHeadAttentionFinalization.register(this, this.__wbg_ptr, this);
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-  free() {
-    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
-    WasmMultiHeadAttentionFinalization.unregister(this);
-    wasm().__wbg_wasmmultiheadattention_free(ptr, 0);
-  }
-  get dim() { return wasm().wasmmultiheadattention_dim(this.__wbg_ptr); }
-  get num_heads() { return wasm().wasmmultiheadattention_num_heads(this.__wbg_ptr); }
-  compute(query, keys, values) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-      const len0 = WASM_VECTOR_LEN;
-      wasm().wasmmultiheadattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-      if (r3) throw takeObject(r2);
-      var v1 = getArrayF32FromWasm0(r0, r1).slice();
-      wasm().__wbindgen_export4(r0, r1 * 4, 4);
-      return v1;
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-}
-
-class WasmFlashAttention {
-  constructor(dim, block_size) {
-    const ret = wasm().wasmflashattention_new(dim, block_size);
-    this.__wbg_ptr = ret >>> 0;
-    WasmFlashAttentionFinalization.register(this, this.__wbg_ptr, this);
-  }
-  free() {
-    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
-    WasmFlashAttentionFinalization.unregister(this);
-    wasm().__wbg_wasmflashattention_free(ptr, 0);
-  }
-  compute(query, keys, values) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-      const len0 = WASM_VECTOR_LEN;
-      wasm().wasmflashattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-      if (r3) throw takeObject(r2);
-      var v1 = getArrayF32FromWasm0(r0, r1).slice();
-      wasm().__wbindgen_export4(r0, r1 * 4, 4);
-      return v1;
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-}
-
-class WasmHyperbolicAttention {
-  constructor(dim, curvature) {
-    const ret = wasm().wasmhyperbolicattention_new(dim, curvature);
-    this.__wbg_ptr = ret >>> 0;
-    WasmHyperbolicAttentionFinalization.register(this, this.__wbg_ptr, this);
-  }
-  free() {
-    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
-    WasmHyperbolicAttentionFinalization.unregister(this);
-    wasm().__wbg_wasmhyperbolicattention_free(ptr, 0);
-  }
-  get curvature() { return wasm().wasmhyperbolicattention_curvature(this.__wbg_ptr); }
-  compute(query, keys, values) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-      const len0 = WASM_VECTOR_LEN;
-      wasm().wasmhyperbolicattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-      if (r3) throw takeObject(r2);
-      var v1 = getArrayF32FromWasm0(r0, r1).slice();
-      wasm().__wbindgen_export4(r0, r1 * 4, 4);
-      return v1;
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-}
-
-class WasmMoEAttention {
-  constructor(dim, num_experts, top_k) {
-    const ret = wasm().wasmmoeattention_new(dim, num_experts, top_k);
-    this.__wbg_ptr = ret >>> 0;
-    WasmMoEAttentionFinalization.register(this, this.__wbg_ptr, this);
-  }
-  free() {
-    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
-    WasmMoEAttentionFinalization.unregister(this);
-    wasm().__wbg_wasmmoeattention_free(ptr, 0);
-  }
-  compute(query, keys, values) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-      const len0 = WASM_VECTOR_LEN;
-      wasm().wasmmoeattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-      if (r3) throw takeObject(r2);
-      var v1 = getArrayF32FromWasm0(r0, r1).slice();
-      wasm().__wbindgen_export4(r0, r1 * 4, 4);
-      return v1;
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-}
-
-class WasmLinearAttention {
-  constructor(dim, num_features) {
-    const ret = wasm().wasmlinearattention_new(dim, num_features || dim);
-    this.__wbg_ptr = ret >>> 0;
-    WasmLinearAttentionFinalization.register(this, this.__wbg_ptr, this);
-  }
-  free() {
-    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
-    WasmLinearAttentionFinalization.unregister(this);
-    wasm().__wbg_wasmlinearattention_free(ptr, 0);
-  }
-  compute(query, keys, values) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-      const len0 = WASM_VECTOR_LEN;
-      wasm().wasmlinearattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-      if (r3) throw takeObject(r2);
-      var v1 = getArrayF32FromWasm0(r0, r1).slice();
-      wasm().__wbindgen_export4(r0, r1 * 4, 4);
-      return v1;
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-}
-
-class WasmLocalGlobalAttention {
-  constructor(dim, local_window, global_tokens) {
-    const ret = wasm().wasmlocalglobalattention_new(dim, local_window || 4, global_tokens || 2);
-    this.__wbg_ptr = ret >>> 0;
-    WasmLocalGlobalAttentionFinalization.register(this, this.__wbg_ptr, this);
-  }
-  free() {
-    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
-    WasmLocalGlobalAttentionFinalization.unregister(this);
-    wasm().__wbg_wasmlocalglobalattention_free(ptr, 0);
-  }
-  compute(query, keys, values) {
-    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-    try {
-      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-      const len0 = WASM_VECTOR_LEN;
-      wasm().wasmlocalglobalattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
-      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-      if (r3) throw takeObject(r2);
-      var v1 = getArrayF32FromWasm0(r0, r1).slice();
-      wasm().__wbindgen_export4(r0, r1 * 4, 4);
-      return v1;
-    } finally {
-      wasm().__wbindgen_add_to_stack_pointer(16);
-    }
-  }
-}
-
-// ── Standalone functions ──────────────────────────────────────────
-
-function cosine_similarity(a, b) {
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    const ptr0 = passArrayF32ToWasm0(a, wasm().__wbindgen_export);
-    const len0 = WASM_VECTOR_LEN;
-    const ptr1 = passArrayF32ToWasm0(b, wasm().__wbindgen_export);
-    const len1 = WASM_VECTOR_LEN;
-    wasm().cosine_similarity(retptr, ptr0, len0, ptr1, len1);
-    var r0 = getDataViewMemory0().getFloat64(retptr + 0, true);
-    var r1 = getDataViewMemory0().getInt32(retptr + 8, true);
-    var r2 = getDataViewMemory0().getInt32(retptr + 12, true);
-    if (r2) throw takeObject(r1);
-    return r0;
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-  }
-}
-
-function normalize(vec) {
-  const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
-  const len0 = WASM_VECTOR_LEN;
-  wasm().normalize(ptr0, len0, addHeapObject(vec));
-}
-
-function l2_norm(vec) {
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
-    const len0 = WASM_VECTOR_LEN;
-    wasm().l2_norm(retptr, ptr0, len0);
-    var r0 = getDataViewMemory0().getFloat64(retptr + 0, true);
-    var r1 = getDataViewMemory0().getInt32(retptr + 8, true);
-    var r2 = getDataViewMemory0().getInt32(retptr + 12, true);
-    if (r2) throw takeObject(r1);
-    return r0;
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-  }
-}
-
-function softmax(vec) {
-  const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
-  const len0 = WASM_VECTOR_LEN;
-  wasm().softmax(ptr0, len0, addHeapObject(vec));
-}
-
-function batch_normalize(vectors, epsilon) {
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    wasm().batch_normalize(retptr, addHeapObject(vectors), isLikeNone(epsilon) ? 0x100000001 : Math.fround(epsilon));
-    var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-    var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-    var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-    var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-    if (r3) throw takeObject(r2);
-    var v1 = getArrayF32FromWasm0(r0, r1).slice();
-    wasm().__wbindgen_export4(r0, r1 * 4, 4);
-    return v1;
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-  }
-}
-
-function pairwise_distances(vectors) {
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    wasm().pairwise_distances(retptr, addHeapObject(vectors));
-    var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-    var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-    var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-    var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-    if (r3) throw takeObject(r2);
-    var v1 = getArrayF32FromWasm0(r0, r1).slice();
-    wasm().__wbindgen_export4(r0, r1 * 4, 4);
-    return v1;
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-  }
-}
-
-function scaled_dot_attention(query, keys, values, scale) {
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
-    const len0 = WASM_VECTOR_LEN;
-    wasm().scaled_dot_attention(retptr, ptr0, len0, addHeapObject(keys), addHeapObject(values), isLikeNone(scale) ? 0x100000001 : Math.fround(scale));
-    var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-    var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-    var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
-    var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
-    if (r3) throw takeObject(r2);
-    var v1 = getArrayF32FromWasm0(r0, r1).slice();
-    wasm().__wbindgen_export4(r0, r1 * 4, 4);
-    return v1;
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-  }
-}
-
-function attention_weights(scores, temperature) {
-  const ptr0 = passArrayF32ToWasm0(scores, wasm().__wbindgen_export);
-  const len0 = WASM_VECTOR_LEN;
-  wasm().attention_weights(ptr0, len0, addHeapObject(scores), isLikeNone(temperature) ? 0x100000001 : Math.fround(temperature));
-}
-
-function available_mechanisms() {
-  const ret = wasm().available_mechanisms();
-  return takeObject(ret);
-}
-
-function random_orthogonal_matrix(dim) {
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    wasm().random_orthogonal_matrix(retptr, dim);
-    var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
-    var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
-    var v1 = getArrayF32FromWasm0(r0, r1).slice();
-    wasm().__wbindgen_export4(r0, r1 * 4, 4);
-    return v1;
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-  }
-}
-
-function rv_init() { wasm().init(); }
-
-function rv_version() {
-  let d0, d1;
-  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
-  try {
-    wasm().version(retptr);
-    d0 = getDataViewMemory0().getInt32(retptr + 0, true);
-    d1 = getDataViewMemory0().getInt32(retptr + 4, true);
-    return getStringFromWasm0(d0, d1);
-  } finally {
-    wasm().__wbindgen_add_to_stack_pointer(16);
-    if (d0 !== undefined) wasm().__wbindgen_export4(d0, d1, 1);
-  }
-}
-
-// ── Collect exports ───────────────────────────────────────────────
-exports.WasmMultiHeadAttention = WasmMultiHeadAttention;
-exports.WasmFlashAttention = WasmFlashAttention;
-exports.WasmHyperbolicAttention = WasmHyperbolicAttention;
-exports.WasmMoEAttention = WasmMoEAttention;
-exports.WasmLinearAttention = WasmLinearAttention;
-exports.WasmLocalGlobalAttention = WasmLocalGlobalAttention;
-exports.cosine_similarity = cosine_similarity;
-exports.normalize = normalize;
-exports.l2_norm = l2_norm;
-exports.softmax = softmax;
-exports.batch_normalize = batch_normalize;
-exports.pairwise_distances = pairwise_distances;
-exports.scaled_dot_attention = scaled_dot_attention;
-exports.attention_weights = attention_weights;
-exports.available_mechanisms = available_mechanisms;
-exports.random_orthogonal_matrix = random_orthogonal_matrix;
-exports.init = rv_init;
-exports.version = rv_version;
-
-// ── Build WASM import object ──────────────────────────────────────
-exports.__wbg_get_imports = function() {
-  const import0 = {
-    __proto__: null,
-    __wbg_Error_4577686b3a6d9b3a: (arg0, arg1) => addHeapObject(Error(getStringFromWasm0(arg0, arg1))),
-    __wbg_String_8564e559799eccda: (arg0, arg1) => {
-      const ret = String(getObject(arg1));
-      const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
-      const len1 = WASM_VECTOR_LEN;
-      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
-      getDataViewMemory0().setInt32(arg0, ptr1, true);
-    },
-    __wbg___wbindgen_boolean_get_18c4ed9422296fff: (arg0) => {
-      const v = getObject(arg0);
-      const ret = typeof v === 'boolean' ? v : undefined;
-      return isLikeNone(ret) ? 0xFFFFFF : ret ? 1 : 0;
-    },
-    __wbg___wbindgen_copy_to_typed_array_5294f8e46aecc086: (arg0, arg1, arg2) => {
-      new Uint8Array(getObject(arg2).buffer, getObject(arg2).byteOffset, getObject(arg2).byteLength).set(getArrayU8FromWasm0(arg0, arg1));
-    },
-    __wbg___wbindgen_debug_string_ddde1867f49c2442: (arg0, arg1) => {
-      const ret = debugString(getObject(arg1));
-      const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
-      const len1 = WASM_VECTOR_LEN;
-      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
-      getDataViewMemory0().setInt32(arg0, ptr1, true);
-    },
-    __wbg___wbindgen_is_function_d633e708baf0d146: (arg0) => typeof getObject(arg0) === 'function',
-    __wbg___wbindgen_is_object_4b3de556756ee8a8: (arg0) => {
-      const val = getObject(arg0);
-      return typeof val === 'object' && val !== null;
-    },
-    __wbg___wbindgen_jsval_loose_eq_1562ceb9af84e990: (arg0, arg1) => getObject(arg0) == getObject(arg1),
-    __wbg___wbindgen_number_get_5854912275df1894: (arg0, arg1) => {
-      const obj = getObject(arg1);
-      const ret = typeof obj === 'number' ? obj : undefined;
-      getDataViewMemory0().setFloat64(arg0 + 8, isLikeNone(ret) ? 0 : ret, true);
-      getDataViewMemory0().setInt32(arg0, !isLikeNone(ret), true);
-    },
-    __wbg___wbindgen_string_get_3e5751597f39a112: (arg0, arg1) => {
-      const obj = getObject(arg1);
-      const ret = typeof obj === 'string' ? obj : undefined;
-      var ptr1 = isLikeNone(ret) ? 0 : passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
-      var len1 = WASM_VECTOR_LEN;
-      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
-      getDataViewMemory0().setInt32(arg0, ptr1, true);
-    },
-    __wbg___wbindgen_throw_39bc967c0e5a9b58: (arg0, arg1) => { throw new Error(getStringFromWasm0(arg0, arg1)); },
-    __wbg_call_73af281463ec8b58: function() { return handleError(function(arg0, arg1) {
-      return addHeapObject(getObject(arg0).call(getObject(arg1)));
-    }, arguments); },
-    __wbg_done_5aad55ec6b1954b1: (arg0) => getObject(arg0).done,
-    __wbg_error_a6fa202b58aa1cd3: (arg0, arg1) => {
-      try { console.error(getStringFromWasm0(arg0, arg1)); }
-      finally { wasm().__wbindgen_export4(arg0, arg1, 1); }
-    },
-    __wbg_error_ad28debb48b5c6bb: (arg0) => console.error(getObject(arg0)),
-    __wbg_get_4920fefd3451364b: function() { return handleError(function(arg0, arg1) {
-      return addHeapObject(Reflect.get(getObject(arg0), getObject(arg1)));
-    }, arguments); },
-    __wbg_get_unchecked_3d0f4b91c8eca4f0: (arg0, arg1) => addHeapObject(getObject(arg0)[arg1 >>> 0]),
-    __wbg_instanceof_ArrayBuffer_15859862b80b732d: (arg0) => {
-      try { return getObject(arg0) instanceof ArrayBuffer; } catch (_) { return false; }
-    },
-    __wbg_instanceof_Uint8Array_2240b7046ac16f05: (arg0) => {
-      try { return getObject(arg0) instanceof Uint8Array; } catch (_) { return false; }
-    },
-    __wbg_isArray_fad08a0d12828686: (arg0) => Array.isArray(getObject(arg0)),
-    __wbg_iterator_fc7ad8d33bab9e26: () => addHeapObject(Symbol.iterator),
-    __wbg_length_5855c1f289dfffc1: (arg0) => getObject(arg0).length,
-    __wbg_length_a31e05262e09b7f8: (arg0) => getObject(arg0).length,
-    __wbg_log_3c5e4b64af29e724: (arg0) => console.log(getObject(arg0)),
-    __wbg_new_09959f7b4c92c246: (arg0) => addHeapObject(new Uint8Array(getObject(arg0))),
-    __wbg_new_227d7c05414eb861: () => addHeapObject(new Error()),
-    __wbg_new_cbee8c0d5c479eac: () => addHeapObject(new Array()),
-    __wbg_next_a5fe6f328f7affc2: (arg0) => addHeapObject(getObject(arg0).next),
-    __wbg_next_e592122bb4ed4c67: function() { return handleError(function(arg0) {
-      return addHeapObject(getObject(arg0).next());
-    }, arguments); },
-    __wbg_prototypesetcall_f034d444741426c3: (arg0, arg1, arg2) => {
-      Uint8Array.prototype.set.call(getArrayU8FromWasm0(arg0, arg1), getObject(arg2));
-    },
-    __wbg_random_2b7bed8995d680fb: () => Math.random(),
-    __wbg_set_4c81cfb5dc3a333c: (arg0, arg1, arg2) => { getObject(arg0)[arg1 >>> 0] = takeObject(arg2); },
-    __wbg_stack_3b0d974bbf31e44f: (arg0, arg1) => {
-      const ret = getObject(arg1).stack;
-      const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
-      const len1 = WASM_VECTOR_LEN;
-      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
-      getDataViewMemory0().setInt32(arg0, ptr1, true);
-    },
-    __wbg_value_667dcb90597486a6: (arg0) => addHeapObject(getObject(arg0).value),
-    __wbindgen_cast_0000000000000001: (arg0, arg1) => addHeapObject(getStringFromWasm0(arg0, arg1)),
-    __wbindgen_object_drop_ref: (arg0) => takeObject(arg0),
-  };
-  return { __proto__: null, "./ruvector_attention_wasm_bg.js": import0 };
-};
-
-})(_mod, () => _wasm);
-
-
-// ── Async WASM init (fetch-based for browsers) ───────────────────
-
-export default async function initWasm() {
-  if (_initialized) return;
-  const wasmUrl = new URL('ruvector_attention_wasm_bg.wasm', import.meta.url);
-  const imports = _mod.__wbg_get_imports();
-  let result;
-  if (typeof WebAssembly.instantiateStreaming === 'function') {
-    try {
-      result = await WebAssembly.instantiateStreaming(fetch(wasmUrl), imports);
-    } catch (e) {
-      // Fallback if streaming fails (e.g. wrong MIME type)
-      const bytes = await (await fetch(wasmUrl)).arrayBuffer();
-      result = await WebAssembly.instantiate(bytes, imports);
-    }
-  } else {
-    const bytes = await (await fetch(wasmUrl)).arrayBuffer();
-    result = await WebAssembly.instantiate(bytes, imports);
-  }
-  _wasm = result.instance.exports;
-  _wasm.__wbindgen_start();
-  _initialized = true;
-}
-
-// ── ESM re-exports ────────────────────────────────────────────────
-// Attention mechanism classes
-export const WasmMultiHeadAttention = _mod.WasmMultiHeadAttention;
-export const WasmFlashAttention = _mod.WasmFlashAttention;
-export const WasmHyperbolicAttention = _mod.WasmHyperbolicAttention;
-export const WasmMoEAttention = _mod.WasmMoEAttention;
-export const WasmLinearAttention = _mod.WasmLinearAttention;
-export const WasmLocalGlobalAttention = _mod.WasmLocalGlobalAttention;
-// Utility functions
-export const cosine_similarity = _mod.cosine_similarity;
-export const normalize = _mod.normalize;
-export const l2_norm = _mod.l2_norm;
-export const softmax = _mod.softmax;
-export const batch_normalize = _mod.batch_normalize;
-export const pairwise_distances = _mod.pairwise_distances;
-export const scaled_dot_attention = _mod.scaled_dot_attention;
-export const attention_weights = _mod.attention_weights;
-export const random_orthogonal_matrix = _mod.random_orthogonal_matrix;
-export const available_mechanisms = _mod.available_mechanisms;
-// Lifecycle
-export const init = _mod.init;
-export const version = _mod.version;
@@ -1,359 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-
-/**
- * Adam optimizer
- */
-export class WasmAdam {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Create a new Adam optimizer
-     *
-     * # Arguments
-     * * `param_count` - Number of parameters
-     * * `learning_rate` - Learning rate
-     */
-    constructor(param_count: number, learning_rate: number);
-    /**
-     * Reset optimizer state
-     */
-    reset(): void;
-    /**
-     * Perform optimization step
-     *
-     * # Arguments
-     * * `params` - Current parameter values (will be updated in-place)
-     * * `gradients` - Gradient values
-     */
-    step(params: Float32Array, gradients: Float32Array): void;
-    /**
-     * Get current learning rate
-     */
-    learning_rate: number;
-}
-
-/**
- * AdamW optimizer (Adam with decoupled weight decay)
- */
-export class WasmAdamW {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Create a new AdamW optimizer
-     *
-     * # Arguments
-     * * `param_count` - Number of parameters
-     * * `learning_rate` - Learning rate
-     * * `weight_decay` - Weight decay coefficient
-     */
-    constructor(param_count: number, learning_rate: number, weight_decay: number);
-    /**
-     * Reset optimizer state
-     */
-    reset(): void;
-    /**
-     * Perform optimization step with weight decay
-     */
-    step(params: Float32Array, gradients: Float32Array): void;
-    /**
-     * Get current learning rate
-     */
-    learning_rate: number;
-    /**
-     * Get weight decay
-     */
-    readonly weight_decay: number;
-}
-
-/**
- * Flash attention mechanism
- */
-export class WasmFlashAttention {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute flash attention
-     */
-    compute(query: Float32Array, keys: any, values: any): Float32Array;
-    /**
-     * Create a new flash attention instance
-     *
-     * # Arguments
-     * * `dim` - Embedding dimension
-     * * `block_size` - Block size for tiling
-     */
-    constructor(dim: number, block_size: number);
-}
-
-/**
- * Hyperbolic attention mechanism
- */
-export class WasmHyperbolicAttention {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute hyperbolic attention
-     */
-    compute(query: Float32Array, keys: any, values: any): Float32Array;
-    /**
-     * Create a new hyperbolic attention instance
-     *
-     * # Arguments
-     * * `dim` - Embedding dimension
-     * * `curvature` - Hyperbolic curvature parameter
-     */
-    constructor(dim: number, curvature: number);
-    /**
-     * Get the curvature
-     */
-    readonly curvature: number;
-}
-
-/**
- * InfoNCE contrastive loss for training
- */
-export class WasmInfoNCELoss {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute InfoNCE loss
-     *
-     * # Arguments
-     * * `anchor` - Anchor embedding
-     * * `positive` - Positive example embedding
-     * * `negatives` - Array of negative example embeddings
-     */
-    compute(anchor: Float32Array, positive: Float32Array, negatives: any): number;
-    /**
-     * Create a new InfoNCE loss instance
-     *
-     * # Arguments
-     * * `temperature` - Temperature parameter for softmax
-     */
-    constructor(temperature: number);
-}
-
-/**
- * Learning rate scheduler
- */
-export class WasmLRScheduler {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Get learning rate for current step
-     */
-    get_lr(): number;
-    /**
-     * Create a new learning rate scheduler with warmup and cosine decay
-     *
-     * # Arguments
-     * * `initial_lr` - Initial learning rate
-     * * `warmup_steps` - Number of warmup steps
-     * * `total_steps` - Total training steps
-     */
-    constructor(initial_lr: number, warmup_steps: number, total_steps: number);
-    /**
-     * Reset scheduler
-     */
-    reset(): void;
-    /**
-     * Advance to next step
-     */
-    step(): void;
-}
-
-/**
- * Linear attention (Performer-style)
- */
-export class WasmLinearAttention {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute linear attention
-     */
-    compute(query: Float32Array, keys: any, values: any): Float32Array;
-    /**
-     * Create a new linear attention instance
-     *
-     * # Arguments
-     * * `dim` - Embedding dimension
-     * * `num_features` - Number of random features
-     */
-    constructor(dim: number, num_features: number);
-}
-
-/**
- * Local-global attention mechanism
- */
-export class WasmLocalGlobalAttention {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute local-global attention
-     */
-    compute(query: Float32Array, keys: any, values: any): Float32Array;
-    /**
-     * Create a new local-global attention instance
-     *
-     * # Arguments
-     * * `dim` - Embedding dimension
-     * * `local_window` - Size of local attention window
-     * * `global_tokens` - Number of global attention tokens
-     */
-    constructor(dim: number, local_window: number, global_tokens: number);
-}
-
-/**
- * Mixture of Experts (MoE) attention
- */
-export class WasmMoEAttention {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute MoE attention
-     */
-    compute(query: Float32Array, keys: any, values: any): Float32Array;
-    /**
-     * Create a new MoE attention instance
-     *
-     * # Arguments
-     * * `dim` - Embedding dimension
-     * * `num_experts` - Number of expert attention mechanisms
-     * * `top_k` - Number of experts to use per query
-     */
-    constructor(dim: number, num_experts: number, top_k: number);
-}
-
-/**
- * Multi-head attention mechanism
- */
-export class WasmMultiHeadAttention {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Compute multi-head attention
-     */
-    compute(query: Float32Array, keys: any, values: any): Float32Array;
-    /**
-     * Create a new multi-head attention instance
-     *
-     * # Arguments
-     * * `dim` - Embedding dimension
-     * * `num_heads` - Number of attention heads
-     */
-    constructor(dim: number, num_heads: number);
-    /**
-     * Get the dimension
-     */
-    readonly dim: number;
-    /**
-     * Get the number of heads
-     */
-    readonly num_heads: number;
-}
-
-/**
- * SGD optimizer with momentum
- */
-export class WasmSGD {
-    free(): void;
-    [Symbol.dispose](): void;
-    /**
-     * Create a new SGD optimizer
-     *
-     * # Arguments
-     * * `param_count` - Number of parameters
-     * * `learning_rate` - Learning rate
-     * * `momentum` - Momentum coefficient (default: 0)
-     */
-    constructor(param_count: number, learning_rate: number, momentum?: number | null);
-    /**
-     * Reset optimizer state
-     */
-    reset(): void;
-    /**
-     * Perform optimization step
-     */
-    step(params: Float32Array, gradients: Float32Array): void;
-    /**
-     * Get current learning rate
-     */
-    learning_rate: number;
-}
-
-/**
- * Compute attention weights from scores
- */
-export function attention_weights(scores: Float32Array, temperature?: number | null): void;
-
-/**
- * Get information about available attention mechanisms
- */
-export function available_mechanisms(): any;
-
-/**
- * Batch normalize vectors
- */
-export function batch_normalize(vectors: any, epsilon?: number | null): Float32Array;
-
-/**
- * Compute cosine similarity between two vectors
- */
-export function cosine_similarity(a: Float32Array, b: Float32Array): number;
-
-/**
- * Initialize the WASM module with panic hook
- */
-export function init(): void;
-
-/**
- * Compute L2 norm of a vector
- */
-export function l2_norm(vec: Float32Array): number;
-
-/**
- * Log a message to the browser console
- */
-export function log(message: string): void;
-
-/**
- * Log an error to the browser console
- */
-export function log_error(message: string): void;
-
-/**
- * Normalize a vector to unit length
- */
-export function normalize(vec: Float32Array): void;
-
-/**
- * Compute pairwise distances between vectors
- */
-export function pairwise_distances(vectors: any): Float32Array;
-
-/**
- * Generate random orthogonal matrix (for initialization)
- */
-export function random_orthogonal_matrix(dim: number): Float32Array;
-
-/**
- * Compute scaled dot-product attention
- *
- * # Arguments
- * * `query` - Query vector as Float32Array
- * * `keys` - Array of key vectors
- * * `values` - Array of value vectors
- * * `scale` - Optional scaling factor (defaults to 1/sqrt(dim))
- */
-export function scaled_dot_attention(query: Float32Array, keys: any, values: any, scale?: number | null): Float32Array;
-
-/**
- * Compute softmax of a vector
- */
-export function softmax(vec: Float32Array): void;
-
-/**
- * Get the version of the ruvector-attention-wasm crate
- */
-export function version(): string;
@@ -1,71 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-export const memory: WebAssembly.Memory;
-export const __wbg_wasmadam_free: (a: number, b: number) => void;
-export const __wbg_wasmadamw_free: (a: number, b: number) => void;
-export const __wbg_wasmflashattention_free: (a: number, b: number) => void;
-export const __wbg_wasmhyperbolicattention_free: (a: number, b: number) => void;
-export const __wbg_wasminfonceloss_free: (a: number, b: number) => void;
-export const __wbg_wasmlinearattention_free: (a: number, b: number) => void;
-export const __wbg_wasmmoeattention_free: (a: number, b: number) => void;
-export const __wbg_wasmmultiheadattention_free: (a: number, b: number) => void;
-export const __wbg_wasmsgd_free: (a: number, b: number) => void;
-export const attention_weights: (a: number, b: number, c: number, d: number) => void;
-export const available_mechanisms: () => number;
-export const batch_normalize: (a: number, b: number, c: number) => void;
-export const cosine_similarity: (a: number, b: number, c: number, d: number, e: number) => void;
-export const l2_norm: (a: number, b: number) => number;
-export const log: (a: number, b: number) => void;
-export const log_error: (a: number, b: number) => void;
-export const normalize: (a: number, b: number, c: number, d: number) => void;
-export const pairwise_distances: (a: number, b: number) => void;
-export const random_orthogonal_matrix: (a: number, b: number) => void;
-export const scaled_dot_attention: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const softmax: (a: number, b: number, c: number) => void;
-export const version: (a: number) => void;
-export const wasmadam_learning_rate: (a: number) => number;
-export const wasmadam_new: (a: number, b: number) => number;
-export const wasmadam_reset: (a: number) => void;
-export const wasmadam_set_learning_rate: (a: number, b: number) => void;
-export const wasmadam_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmadamw_new: (a: number, b: number, c: number) => number;
-export const wasmadamw_reset: (a: number) => void;
-export const wasmadamw_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmadamw_weight_decay: (a: number) => number;
-export const wasmflashattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmflashattention_new: (a: number, b: number) => number;
-export const wasmhyperbolicattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmhyperbolicattention_curvature: (a: number) => number;
-export const wasmhyperbolicattention_new: (a: number, b: number) => number;
-export const wasminfonceloss_compute: (a: number, b: number, c: number, d: number, e: number, f: number, g: number) => void;
-export const wasminfonceloss_new: (a: number) => number;
-export const wasmlinearattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmlinearattention_new: (a: number, b: number) => number;
-export const wasmlocalglobalattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmlocalglobalattention_new: (a: number, b: number, c: number) => number;
-export const wasmlrscheduler_get_lr: (a: number) => number;
-export const wasmlrscheduler_new: (a: number, b: number, c: number) => number;
-export const wasmlrscheduler_reset: (a: number) => void;
-export const wasmlrscheduler_step: (a: number) => void;
-export const wasmmoeattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmmoeattention_new: (a: number, b: number, c: number) => number;
-export const wasmmultiheadattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const wasmmultiheadattention_dim: (a: number) => number;
-export const wasmmultiheadattention_new: (a: number, b: number, c: number) => void;
-export const wasmmultiheadattention_num_heads: (a: number) => number;
-export const wasmsgd_learning_rate: (a: number) => number;
-export const wasmsgd_new: (a: number, b: number, c: number) => number;
-export const wasmsgd_reset: (a: number) => void;
-export const wasmsgd_set_learning_rate: (a: number, b: number) => void;
-export const wasmsgd_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
-export const init: () => void;
-export const wasmadamw_set_learning_rate: (a: number, b: number) => void;
-export const wasmadamw_learning_rate: (a: number) => number;
-export const __wbg_wasmlocalglobalattention_free: (a: number, b: number) => void;
-export const __wbg_wasmlrscheduler_free: (a: number, b: number) => void;
-export const __wbindgen_export: (a: number, b: number) => number;
-export const __wbindgen_export2: (a: number, b: number, c: number, d: number) => number;
-export const __wbindgen_export3: (a: number) => void;
-export const __wbindgen_export4: (a: number, b: number, c: number) => void;
-export const __wbindgen_add_to_stack_pointer: (a: number) => number;
-export const __wbindgen_start: () => void;
Author	SHA1	Message	Date
ruv	0223ef6d2e	docs: add ADR-059 live ESP32 CSI pipeline + update README with demo links - ADR-059: Documents end-to-end ESP32 → sensing server → browser pipeline - README: Add dual-modal pose fusion demo link, update ADR count to 49 - References issue #245 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 17:40:16 -04:00
ruv	2f5e7ffb41	feat: live ESP32 CSI pipeline + auto-connect WebSocket - Add auto-connect to local sensing server WebSocket (ws://localhost:8765) - Demo shows "Live ESP32" when connected to real CSI data - Add build_firmware.ps1 for native Windows ESP-IDF builds (no Docker) - Add read_serial.ps1 for ESP32 serial monitor Pipeline: ESP32 → UDP:5005 → sensing-server → WS:8765 → browser demo Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 17:37:27 -04:00
ruv	4ce8ffc465	fix: video fills available space + correct WASM path resolution - Remove fixed aspect-ratio and max-height from video panel so it fills the available viewport space without scrolling - Grid uses 1fr row for content area, overflow:hidden on main grid - Fix WASM path: resolve relative to JS module file using import.meta.url instead of hardcoded ./pkg/ which resolved incorrectly on gh-pages - Responsive: mobile still gets aspect-ratio constraint Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 16:16:07 -04:00
ruv	3be63a7589	fix: motion-responsive skeleton + through-wall CSI tracking - Pose decoder now uses per-cell motion grid to track actual arm/head positions — raising arms moves the skeleton's arms, head follows lateral movement - Motion grid (10x8 cells) tracks intensity per body zone: head, left/right arm upper/mid, legs - Through-wall mode: when person exits frame, CSI maintains presence with slow decay (~10s) and skeleton drifts in exit direction - CSI simulator persists sensing after video loss, ghost pose renders with decreasing confidence - Reduced temporal smoothing (0.45) for faster response to movement Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 16:10:29 -04:00
ruv	c4e640c812	feat: dual-modal WASM browser pose estimation demo (ADR-058) Live webcam video + WiFi CSI fusion for real-time pose estimation. Two parallel CNN pipelines (ruvector-cnn-wasm) with attention-weighted fusion and dynamic confidence gating. Three modes: Dual, Video-only, CSI-only. Includes pre-built WASM package (~52KB) for browser deployment. - ADR-058: Dual-modal architecture design - ui/pose-fusion.html: Main demo page with dark theme UI - 7 JS modules: video-capture, csi-simulator, cnn-embedder, fusion-engine, pose-decoder, canvas-renderer, main orchestrator - Pre-built ruvector-cnn-wasm WASM package for browser - CSI heatmap, embedding space visualization, latency metrics - WebSocket support for live ESP32 CSI data - Navigation link added to main dashboard Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 14:26:39 -04:00