Publish MC N=8: 1000 trajectories and 1855 targets

app.js

@@ -2,7 +2,7 @@
 
 const $ = (id) => document.getElementById(id);
 const state = {index:null, episode:null, activeStep:null, query:null, tab:"trajectory", cache:new Map(), loadId:0};
-const number = (x, digits=2) => typeof x === "number" ? x.toFixed(digits) : "—";
+const number = (x, digits=3) => typeof x === "number" ? x.toFixed(digits) : "—";
 const text = (tag, value, cls) => {
   const node = document.createElement(tag);
   node.textContent = value;
@@ -63,21 +63,23 @@ function renderList() {
 }
 async function loadEpisode(id, queryIndex=null) {
   const generation = ++state.loadId;
+  const snapshot = state.index.summary.created_at;
+  const cacheKey = snapshot + "/" + id;
   $("error").hidden = true;
   try {
-    if (!state.cache.has(id)) {
+    if (!state.cache.has(cacheKey)) {
       $("trajectory-view").replaceChildren(text("div", "Loading trajectory…", "loading"));
-      const response = await fetch("data/episodes/" + encodeURIComponent(id) + ".json.gz");
+      const response = await fetch("data/episodes/" + encodeURIComponent(id) + ".json.gz?snapshot=" + encodeURIComponent(snapshot));
       if (!response.ok) throw new Error("Trajectory request returned " + response.status);
       let bytes = new Uint8Array(await response.arrayBuffer());
       if (bytes[0] === 31 && bytes[1] === 139) {
         const stream = new Blob([bytes]).stream().pipeThrough(new DecompressionStream("gzip"));
         bytes = new Uint8Array(await new Response(stream).arrayBuffer());
       }
-      state.cache.set(id, JSON.parse(new TextDecoder().decode(bytes)));
+      state.cache.set(cacheKey, JSON.parse(new TextDecoder().decode(bytes)));
     }
     if (generation !== state.loadId) return;
-    state.episode = state.cache.get(id);
+    state.episode = state.cache.get(cacheKey);
     const queries = Object.values(state.episode.queries);
     const selected = queryIndex !== null ? state.episode.queries[String(queryIndex)] :
       queries.find((q) => q.target_value !== null) || queries[0];
@@ -119,7 +121,7 @@ function renderTrajectory() {
   const header = node("div", "episode-header", [
     node("div", "", [title, node("div", "episode-subtitle", [
       text("span", episode.split === "train" ? "Training split" : "Validation split"),
-      text("span", "Seed " + episode.seed), text("span", episode.navigation_steps + " / 10 navigation moves"),
+      text("span", "Seed " + episode.seed), text("span", episode.navigation_steps + " / " + (state.index.summary.horizon ?? 10) + " navigation moves"),
       text("span", episode.decisions + " decisions")
     ])]), badge(episode.success ? "✓ Goal reached" : episode.termination, episode.success ? "green" : "")
   ]);
@@ -283,7 +285,8 @@ function renderCalibration() {
   const ylabel = svgNode("text", {transform:"translate(17 210) rotate(-90)", "text-anchor":"middle"}); ylabel.textContent = "MC target · observed success fraction";
   chart.append(xlabel, ylabel);
   const plot = node("section", "panel", [heading("Predictions vs. MC targets", "Click a point to inspect its call"), node("div", "plot-wrap", [chart]), text("div", "Green: training · Purple: validation · Dashed line: agreement. Points can overlap at identical values.", "chart-caption")]);
-  const bins = [0, .25, .5, .75, 1].map((v) => ({value:v, count:points.filter((p) => p.target === v).length}));
+  const samples = state.index.summary.samples;
+  const bins = Array.from({length:samples + 1}, (_, i) => ({value:i / samples, count:points.filter((p) => p.target === i / samples).length}));
   const max = Math.max(1, ...bins.map((b) => b.count));
   const histogram = node("div", "histogram");
   for (const bin of bins) {
@@ -292,7 +295,7 @@ function renderCalibration() {
   }
   const notes = node("div", "method-note", [
     text("strong", "Read these as sample outcomes."),
-    text("p", "Each target averages four terminal success outcomes. A target of 0 or 1 is not proof that the true probability is exactly 0 or 1."),
+    text("p", "Each target averages " + samples + " terminal success outcomes. A target of 0 or 1 is not proof that the true probability is exactly 0 or 1."),
     text("p", "Predictions come from the prompted Q tool. No trained scalar value model is represented here. Only sampled calls contribute training targets."),
     text("p", "The MC continuation policy can use prompted Q tools; the unchanged historical estimator prompt describes tool-free continuations.")
   ]);

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