diff --git "a/index.html" "b/index.html"
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+++ "b/index.html"
@@ -504,13 +504,15 @@
       <div class="section-header"><h2>Accuracy vs On-target Latency</h2>
         <span class="section-note">End-to-end per-image latency measured by the <a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler</a> on deployment hardware</span>
       </div>
-      <div id="timing-host">
-        <p style="font-family:var(--serif);color:var(--text-muted);font-size:0.92rem">
-          On-target latency results are being collected across the platform matrix.
-          See the <a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler documentation</a>
-          for how profiling and validation are performed.
-        </p>
-      </div>
+      <div class="chart-host" style="height:420px"><canvas id="chart-det-lat"></canvas></div>
+      <p style="font-family:var(--serif);color:var(--text-muted);font-size:0.82rem;margin-top:0.5rem">
+        Each point is a public validation session: color = platform, shape = model family
+        (● YOLOv5 · ■ YOLOv8 · ▲ YOLO11 · ◆ YOLO26). X axis is end-to-end per-image latency
+        (load → decode → preprocess → inference → postprocess) on a log scale — lower is better, so
+        the top-left is the sweet spot. For the i.MX 95 Neutron runner the latency-optimized pipeline
+        (<code>--serialize-core</code>) is shown here; its throughput-optimized counterpart is on the
+        chart above.
+      </p>
     </div>
   </section>
 
@@ -646,8 +648,8 @@
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 <script>
 "use strict";
 
@@ -958,10 +960,15 @@ function renderRoadmap() {
     if (entries.length === 0) continue;
     const label = entries[0].version_label;
 
-    // Validated platform coverage for this family across its tasks.
+    // Validated platform coverage for this family across its tasks (nano/small/
+    // medium only). N/A cells — platform+model combinations declared unsupported
+    // (e.g. yolo11/26 on i.MX 95 Neutron) — are excluded from the denominator so
+    // the percentage reflects real, attainable coverage rather than being dragged
+    // down by combinations that will never have a result.
     let covered = 0, possible = 0;
     for (const m of entries) {
       for (const p of PLATFORM_ROWS) {
+        if (UNSUPPORTED_CELLS.has(`${m.task_key}|${v}|${p.key}`)) continue;
         for (const sz of COVERAGE_SIZES) {
           possible += 1;
           if (seen[`${m.task_key}|${v}|${sz}|${p.key}`]) covered += 1;
@@ -1179,11 +1186,20 @@ function chartDetectionBar() {
 
 // Accuracy vs measured pipelined throughput — color by platform, shape by
 // family, log-scale X (mirrors the EFPI report's throughput figure).
+// Accuracy floor for the scatter charts only: a model whose on-target mAP is
+// below this is a broken/degraded outlier that compresses the useful axis range.
+// Kept out of the charts (leaderboards/tables still show every measured number).
+const CHART_MAP_FLOOR = 20;
+
 function chartDetFps() {
   const canvas = document.getElementById("chart-det-fps");
   if (!canvas) return;
+  // Throughput chart: throughput-optimized sessions (+ single-mode platforms);
+  // the latency-optimized runs belong on the latency chart.
   const usable = (STUDIO?.sessions ?? []).filter(s =>
-    s.task_key === "det" && s.fps_median > 0 && s.det_map_50_95 != null);
+    s.task_key === "det" && s.fps_median > 0 && s.det_map_50_95 != null
+    && s.det_map_50_95 >= CHART_MAP_FLOOR
+    && s.pipeline_mode !== "latency");
   if (usable.length === 0) return;
 
   const byPlatform = {};
@@ -1236,6 +1252,67 @@ function chartDetFps() {
   });
 }
 
+function chartDetLatency() {
+  const canvas = document.getElementById("chart-det-lat");
+  if (!canvas) return;
+  // Latency chart: latency-optimized sessions (+ single-mode platforms); the
+  // throughput-optimized runs belong on the throughput chart above.
+  const usable = (STUDIO?.sessions ?? []).filter(s =>
+    s.task_key === "det" && s.e2e_latency_ms > 0 && s.det_map_50_95 != null
+    && s.det_map_50_95 >= CHART_MAP_FLOOR
+    && s.pipeline_mode !== "throughput");
+  if (usable.length === 0) return;
+
+  const byPlatform = {};
+  for (const s of usable) {
+    const p = platformRowForSession(s);
+    if (!p) continue;
+    (byPlatform[p.key] ||= []).push(s);
+  }
+
+  const datasets = PLATFORM_ROWS.filter(p => byPlatform[p.key]).map(p => {
+    const pts = byPlatform[p.key];
+    return {
+      label: `${p.label} ${p.detail.includes("CoreML") ? p.detail.replace("ONNX + ", "") : ""}`.trim(),
+      data: pts.map(s => ({
+        x: s.e2e_latency_ms,
+        y: s.det_map_50_95,
+        name: sessionModelName(s),
+        flagged: isBelowExpectations(s),
+      })),
+      backgroundColor: p.color,
+      borderColor: p.color,
+      pointStyle: pts.map(s => FAMILY_SHAPES[s.version_key] ?? "circle"),
+      pointRadius: 7, pointHoverRadius: 10,
+    };
+  });
+
+  destroyChart("detLat");
+  charts.detLat = new Chart(canvas, {
+    type: "scatter",
+    data: { datasets },
+    options: {
+      responsive: true, maintainAspectRatio: false,
+      plugins: {
+        legend: { display: true, position: "bottom" },
+        tooltip: {
+          callbacks: {
+            label: (ctx) => `${ctx.raw.name} on ${ctx.dataset.label}: ${ctx.parsed.y.toFixed(1)} mAP @ ${ctx.parsed.x.toFixed(0)} ms${ctx.raw.flagged ? " ⚠" : ""}`,
+          },
+        },
+      },
+      scales: {
+        x: { type: "logarithmic",
+             title: { display: true, text: "End-to-end per-image latency (ms, log scale — lower is better)" },
+             ticks: { font: { family: "monospace", size: 10 },
+                      callback: (v) => ([10,20,50,100,200,500,1000,2000].includes(v) ? String(v) : null) } },
+        y: { title: { display: true, text: "mAP@0.5-0.95 (%)" }, beginAtZero: false,
+             ticks: { font: { family: "monospace", size: 10 } } },
+      },
+    },
+  });
+}
+
 function chartDetectionScatter() {
   const data = VERSION_ORDER.filter(v => SNAPSHOT.det_bench[v]?.n).map(v => ({
     label: v.toUpperCase(),
@@ -1640,6 +1717,7 @@ window.addEventListener("DOMContentLoaded", () => {
   chartDetectionBar();
   chartDetectionScatter();
   chartDetFps();
+  chartDetLatency();
   chartSegBar();
   chartSegScatter();
   renderPlatformThroughput();