fixed legends for plots

app/presentation/se2026/charts/cost-efficiency.html

@@ -108,8 +108,11 @@
         'distill': 'Distill', 'diverse_qa_pairs': 'Diverse QA',
         'extract_knowledge': 'Extract Knowledge', 'knowledge_list': 'Knowledge List',
         'wikipedia_style_rephrasing': 'Wikipedia Style',
-        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite'
+        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite',
+        'explanation': 'Explanation', 'narrative': 'Narrative'
       };
+      const resolvePromptLabel = (key) =>
+        PROMPT_LABELS[key] || PROMPT_LABELS[Object.keys(PROMPT_LABELS).find(k => key.startsWith(k + '-'))] || key;
       const CAT_MAP = { 'format': 'Format', 'nemotron': 'Nemotron', 'rewire': 'REWIRE' };
       const getFamily = (m) => {
         const ml = m.toLowerCase();
@@ -160,7 +163,7 @@
         return {
           run: d.run,
           cat: CAT_MAP[cat] || cat,
-          prompt: PROMPT_LABELS[promptKey] || promptKey,
+          prompt: resolvePromptLabel(promptKey),
           model: d.model.split('/').pop(),
           source: SOURCE_MAP[d.source_dataset] || d.source_dataset,
           family: getFamily(d.model),

app/src/content/embeds/compression-performance.html

@@ -98,8 +98,12 @@
         'distill': 'Distill', 'diverse_qa_pairs': 'Diverse QA',
         'extract_knowledge': 'Extract Knowledge', 'knowledge_list': 'Knowledge List',
         'wikipedia_style_rephrasing': 'Wikipedia Style',
-        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite'
+        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite',
+        'explanation': 'Explanation', 'narrative': 'Narrative'
       };
+      // Model-specific prompt variants like "explanation-falcon3-1b-hq" -> base "explanation"
+      const resolvePromptLabel = (key) =>
+        PROMPT_LABELS[key] || PROMPT_LABELS[Object.keys(PROMPT_LABELS).find(k => key.startsWith(k + '-'))] || key;
 
       const getFamily = (m) => {
         const ml = m.toLowerCase();
@@ -112,11 +116,14 @@
         return 'Other';
       };
 
-      // Color by prompt type
-      const allPromptKeys = [...new Set(rawData.map(d => d.prompt.split('/')[1].replace('.md', '')))].sort();
+      // Color by prompt type (using display labels so variants share colors)
+      const allPromptLabels = [...new Set(rawData.map(d => {
+        const key = d.prompt.split('/')[1].replace('.md', '');
+        return resolvePromptLabel(key);
+      }))].sort();
       const promptColors = {};
-      const cat = window.ColorPalettes ? window.ColorPalettes.getColors('categorical', allPromptKeys.length) : d3.schemeTableau10.concat(d3.schemePastel1);
-      allPromptKeys.forEach((k, i) => { promptColors[PROMPT_LABELS[k] || k] = cat[i % cat.length]; });
+      const cat = window.ColorPalettes ? window.ColorPalettes.getColors('categorical', allPromptLabels.length) : d3.schemeTableau10.concat(d3.schemePastel1);
+      allPromptLabels.forEach((label, i) => { promptColors[label] = cat[i % cat.length]; });
 
       const METRIC_NAMES = {
         'agg_score_macro': 'Aggregate Score (Macro)',
@@ -148,7 +155,7 @@
         const promptKey = promptFile.replace('.md', '');
         return {
           run: d.run,
-          prompt: PROMPT_LABELS[promptKey] || promptKey,
+          prompt: resolvePromptLabel(promptKey),
           model: d.model.split('/').pop(),
           source: SOURCE_MAP[d.source_dataset] || d.source_dataset,
           family: getFamily(d.model),

app/src/content/embeds/cost-efficiency.html

@@ -103,8 +103,11 @@
         'distill': 'Distill', 'diverse_qa_pairs': 'Diverse QA',
         'extract_knowledge': 'Extract Knowledge', 'knowledge_list': 'Knowledge List',
         'wikipedia_style_rephrasing': 'Wikipedia Style',
-        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite'
+        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite',
+        'explanation': 'Explanation', 'narrative': 'Narrative'
       };
+      const resolvePromptLabel = (key) =>
+        PROMPT_LABELS[key] || PROMPT_LABELS[Object.keys(PROMPT_LABELS).find(k => key.startsWith(k + '-'))] || key;
       const CAT_MAP = { 'format': 'Format', 'nemotron': 'Nemotron', 'rewire': 'REWIRE' };
       const getFamily = (m) => {
         const ml = m.toLowerCase();
@@ -156,7 +159,7 @@
         return {
           run: d.run,
           cat: CAT_MAP[cat] || cat,
-          prompt: PROMPT_LABELS[promptKey] || promptKey,
+          prompt: resolvePromptLabel(promptKey),
           model: d.model.split('/').pop(),
           source: SOURCE_MAP[d.source_dataset] || d.source_dataset,
           family: getFamily(d.model),

app/src/content/embeds/score-shift.html

@@ -98,8 +98,11 @@
         'distill': 'Distill', 'diverse_qa_pairs': 'Diverse QA',
         'extract_knowledge': 'Extract Knowledge', 'knowledge_list': 'Knowledge List',
         'wikipedia_style_rephrasing': 'Wikipedia Style',
-        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite'
+        'guided_rewrite_improved': 'Guided Rewrite+', 'guided_rewrite_original': 'Guided Rewrite',
+        'explanation': 'Explanation', 'narrative': 'Narrative'
       };
+      const resolvePromptLabel = (key) =>
+        PROMPT_LABELS[key] || PROMPT_LABELS[Object.keys(PROMPT_LABELS).find(k => key.startsWith(k + '-'))] || key;
 
       const getFamily = (m) => {
         const ml = m.toLowerCase();
@@ -112,10 +115,13 @@
         return 'Other';
       };
 
-      const allPromptKeys = [...new Set(rawData.map(d => d.prompt.split('/')[1].replace('.md', '')))].sort();
+      const allPromptLabels = [...new Set(rawData.map(d => {
+        const key = d.prompt.split('/')[1].replace('.md', '');
+        return resolvePromptLabel(key);
+      }))].sort();
       const promptColors = {};
-      const cat = window.ColorPalettes ? window.ColorPalettes.getColors('categorical', allPromptKeys.length) : d3.schemeTableau10.concat(d3.schemePastel1);
-      allPromptKeys.forEach((k, i) => { promptColors[PROMPT_LABELS[k] || k] = cat[i % cat.length]; });
+      const cat = window.ColorPalettes ? window.ColorPalettes.getColors('categorical', allPromptLabels.length) : d3.schemeTableau10.concat(d3.schemePastel1);
+      allPromptLabels.forEach((label, i) => { promptColors[label] = cat[i % cat.length]; });
 
       const SCORE_MODES = [
         { key: 'dclm', label: 'DCLM Score', inputKey: 'input_dclm_score', outputKey: 'output_dclm_score' },
@@ -137,7 +143,7 @@
           const promptKey = promptFile.replace('.md', '');
           return {
             run: d.run,
-            prompt: PROMPT_LABELS[promptKey] || promptKey,
+            prompt: resolvePromptLabel(promptKey),
             model: d.model.split('/').pop(),
             source: SOURCE_MAP[d.source_dataset] || d.source_dataset,
             family: getFamily(d.model),

app/src/content/embeds/verbosity.html

@@ -63,8 +63,11 @@
                     'extract_knowledge': 'Extract Knowledge', 'knowledge_list': 'Knowledge List',
                     'wikipedia_style_rephrasing': 'Wikipedia Style',
                     'guided_rewrite_improved': 'Guided Rewrite+',
-                    'guided_rewrite_original': 'Guided Rewrite'
+                    'guided_rewrite_original': 'Guided Rewrite',
+                    'explanation': 'Explanation', 'narrative': 'Narrative'
                 };
+                const resolvePromptLabel = (key) =>
+                    PROMPT_LABELS[key] || PROMPT_LABELS[Object.keys(PROMPT_LABELS).find(k => key.startsWith(k + '-'))] || key;
                 const CAT_MAP = { 'format': 'Format', 'nemotron': 'Nemotron', 'rewire': 'REWIRE' };
 
                 const getFamily = (m) => {
@@ -84,7 +87,7 @@
                     return {
                         idx: i,
                         cat: CAT_MAP[cat] || cat,
-                        prompt: PROMPT_LABELS[promptKey] || promptKey,
+                        prompt: resolvePromptLabel(promptKey),
                         model: d.model.split('/').pop(),
                         source: SOURCE_MAP[d.source_dataset] || d.source_dataset,
                         family: getFamily(d.model),