Docs: Update benchmark results

tests/6_csv_processor/outputs/anthropic_claude-sonnet-4.5 TEMP_0.4.js

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+async function processCSV(csv, config) {
+  const { parse } = await import('https://cdn.skypack.dev/papaparse@5.4.1');
+  
+  const { filterColumn: fc, filterValue: fv, groupBy: gb, aggregateColumn: ac, operation: op } = config;
+  const { data } = parse(csv, { header: true, skipEmptyLines: true });
+  
+  const filtered = data.filter(r => r[fc] == fv);
+  const grouped = filtered.reduce((acc, row) => {
+    const key = row[gb];
+    if (!acc[key]) acc[key] = [];
+    acc[key].push(row);
+    return acc;
+  }, {});
+  
+  return Object.entries(grouped).map(([key, rows]) => {
+    const vals = rows.map(r => parseFloat(r[ac]) || 0);
+    const result = op === 'sum' 
+      ? vals.reduce((a, b) => a + b, 0)
+      : op === 'avg'
+      ? vals.reduce((a, b) => a + b, 0) / vals.length
+      : vals.length;
+    
+    return { [gb]: key, result };
+  });
+}
+export default processCSV;

tests/6_csv_processor/outputs/anthropic_claude-sonnet-4.5 TEMP_0.7.js

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+const processCSV = async (csv, { filterColumn, filterValue, groupBy, aggregateColumn, operation }) => {
+  const { default: Papa } = await import('https://cdn.jsdelivr.net/npm/papaparse@5.4.1/+esm');
+  
+  const { data } = Papa.parse(csv, { header: true, skipEmptyLines: true });
+  
+  const filtered = data.filter(row => row[filterColumn] == filterValue);
+  
+  const grouped = filtered.reduce((acc, row) => {
+    const key = row[groupBy];
+    if (!acc[key]) acc[key] = [];
+    acc[key].push(row);
+    return acc;
+  }, {});
+  
+  return Object.entries(grouped).map(([key, rows]) => {
+    let result;
+    
+    if (operation === 'count') {
+      result = rows.length;
+    } else {
+      const values = rows.map(r => parseFloat(r[aggregateColumn]) || 0);
+      result = operation === 'sum' 
+        ? values.reduce((a, b) => a + b, 0)
+        : values.reduce((a, b) => a + b, 0) / values.length;
+    }
+    
+    return { [groupBy]: key, [operation]: result };
+  });
+};
+export default processCSV;

tests/6_csv_processor/outputs/anthropic_claude-sonnet-4.5.js

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+async function processCSV(csvString, config) {
+  const { parse } = await import('https://cdn.skypack.dev/papaparse@5.4.1');
+  
+  const { filterColumn: fc, filterValue: fv, groupBy: gb, aggregateColumn: ac, operation: op } = config;
+  
+  const { data } = parse(csvString, { header: true, skipEmptyLines: true });
+  
+  const filtered = data.filter(row => row[fc] == fv);
+  
+  const grouped = filtered.reduce((acc, row) => {
+    const key = row[gb];
+    if (!acc[key]) acc[key] = [];
+    acc[key].push(row);
+    return acc;
+  }, {});
+  
+  return Object.entries(grouped).map(([key, rows]) => {
+    let result;
+    if (op === 'count') {
+      result = rows.length;
+    } else {
+      const values = rows.map(r => parseFloat(r[ac]) || 0);
+      result = op === 'sum' 
+        ? values.reduce((a, b) => a + b, 0)
+        : values.reduce((a, b) => a + b, 0) / values.length;
+    }
+    return { [gb]: key, result };
+  });
+}
+export default processCSV;

tests/6_csv_processor/outputs/google_gemini-2.5-pro TEMP_0.7.js

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+const processCSV = async (csvString, {
+    filterColumn,
+    filterValue,
+    groupBy,
+    aggregateColumn,
+    operation
+}) => {
+    const { default: Papa } = await import('https://cdn.jsdelivr.net/npm/papaparse@5.4.1/+esm');
+
+    const aggregations = {
+        sum: g => g.s,
+        count: g => g.c,
+        avg: g => g.c ? g.s / g.c : 0,
+    };
+
+    const groupedData = Papa.parse(csvString, {
+        header: true,
+        skipEmptyLines: true,
+        dynamicTyping: true,
+    }).data
+    .filter(row => row[filterColumn] === filterValue)
+    .reduce((acc, row) => {
+        const key = row[groupBy];
+        const val = row[aggregateColumn];
+
+        acc[key] = acc[key] || { s: 0, c: 0 };
+        if (typeof val === 'number' && !isNaN(val)) {
+            acc[key].s += val;
+        }
+        acc[key].c++;
+
+        return acc;
+    }, {});
+
+    return Object.entries(groupedData).map(([key, group]) => ({
+        [groupBy]: /^-?\d+(\.\d+)?$/.test(key) ? Number(key) : key,
+        [operation]: aggregations[operation](group),
+    }));
+};
+export default processCSV;

tests/6_csv_processor/outputs/openai_gpt-5-codex.js

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+let cache;
+async function processCSV(csv,cfg){
+ const {csvParse,rollups}=await (cache??=import('https://cdn.jsdelivr.net/npm/d3@7/+esm'));
+ const {filterColumn,filterValue,groupBy,aggregateColumn,operation}=cfg;
+ if(!['sum','avg','count'].includes(operation))throw new Error('Unsupported operation');
+ if(!csv||!filterColumn||!groupBy)throw new Error('Missing essentials');
+ if(operation!=='count'&&!aggregateColumn)throw new Error('Missing aggregateColumn');
+ const rows=csvParse(csv).filter(r=>r[filterColumn]==filterValue);
+ if(!rows.length)return[];
+ const fn=operation==='count'?v=>v.length:v=>{
+  let s=0,n=0;
+  for(const row of v){
+   const val=+row[aggregateColumn];
+   if(!Number.isNaN(val)){s+=val;n++;}
+  }
+  return operation==='sum'?s:n?s/n:0;
+ };
+ return rollups(rows,fn,r=>r[groupBy]).map(([k,v])=>({[groupBy]:k,[operation]:v}));
+}
+export default processCSV;

tests/6_csv_processor/outputs/openrouter_polaris-alpha.js

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+async function processCSV(csv, cfg) {
+  const {filterColumn,filterValue,groupBy,aggregateColumn,operation} = cfg
+  if(!csv||!filterColumn||!groupBy||!operation) throw new Error('Invalid configuration')
+  const [{parse},{default:lodash}] = await Promise.all([
+    import('https://cdn.jsdelivr.net/npm/papaparse@5.4.1/+esm'),
+    import('https://cdn.jsdelivr.net/npm/lodash-es@4.17.21/+esm')
+  ])
+  const {data,errors} = parse(csv,{header:true,skipEmptyLines:true,dynamicTyping:true})
+  if(errors?.length) throw new Error('CSV parse error')
+  const rows = data.filter(r=>r[filterColumn]===filterValue)
+  if(!rows.length) return []
+  const grouped = lodash.groupBy(rows,r=>r[groupBy])
+  const out = []
+  for(const k in grouped){
+    const g = grouped[k]
+    let v
+    if(operation==='count') v = g.length
+    else{
+      const nums = g.map(r=>Number(r[aggregateColumn])).filter(n=>Number.isFinite(n))
+      if(!nums.length) { v = operation==='sum'?0:null }
+      else{
+        const s = nums.reduce((a,b)=>a+b,0)
+        v = operation==='sum'?s:s/nums.length
+      }
+    }
+    out.push({[groupBy]:k,result:v})
+  }
+  return out
+}
+export default processCSV;