Improve MemFuse recall via hybrid ranking and candidate generation.

Add RRF fusion with English word-level lexical scoring, tiered keyword fetch, and vector margin expansion (0.15/200) to fix pre-rank truncation; wire DashScope embedding bench path and update baseline to 28.8% recall@20.

Co-authored-by: Cursor <cursoragent@cursor.com>

scripts/run-memory-v2-memfuse-bench.mjs

@@ -25,6 +25,7 @@ import {
   summarizeMemFuseBench,
 } from '../memory-v2-memfuse-bench.mjs';
 import { resolveEmbeddingModuleSpecifier } from '../memory-v2-recall-benchmark.mjs';
+import { loadMemindEnvFiles } from './memind-runtime-profile.mjs';
 
 function parseArgs(argv) {
   const options = {
@@ -102,6 +103,12 @@ async function loadEmbedder(specifier) {
   if (typeof embed !== 'function') {
     throw new Error(`Embedding module must export embedText, embedQuery or default: ${specifier}`);
   }
+  if (typeof imported.prefetchEmbedTexts === 'function') {
+    embed.prefetchEmbedTexts = imported.prefetchEmbedTexts.bind(imported);
+  }
+  if (typeof imported.flushEmbeddingCache === 'function') {
+    embed.flushEmbeddingCache = imported.flushEmbeddingCache.bind(imported);
+  }
   return embed;
 }
 
@@ -168,6 +175,7 @@ function printReport(report) {
 }
 
 async function main() {
+  loadMemindEnvFiles(process.cwd());
   const options = parseArgs(process.argv.slice(2));
   if (options.help) {
     usage();
@@ -195,6 +203,27 @@ async function main() {
   const embedText = options.embeddingModule
     ? await loadEmbedder(options.embeddingModule)
     : null;
+  const prefetchEmbedTexts = embedText?.prefetchEmbedTexts ?? null;
+
+  if (options.embeddingModule) {
+    const resolved = resolveEmbeddingModuleSpecifier(options.embeddingModule);
+    const imported = await import(resolved);
+    if (typeof imported.probeEmbeddingAvailability === 'function') {
+      const probe = await imported.probeEmbeddingAvailability();
+      if (!probe.available) {
+        process.stderr.write(
+          `Embedding probe failed (${probe.reason}).\n` +
+            'Set MEMIND_EMBEDDING_API_KEY / OPENAI_API_KEY, or MEMIND_EMBEDDING_PROVIDER=ollama.\n',
+        );
+        return options.strict ? 1 : 0;
+      }
+      if (!options.quiet) {
+        process.stderr.write(
+          `embedding probe ok: provider=${probe.provider} model=${probe.model} dims=${probe.dimensions}\n`,
+        );
+      }
+    }
+  }
 
   let lastScenario = null;
   const report = await runMemFuseBench({
@@ -207,6 +236,7 @@ async function main() {
     candidateLimit: options.candidateLimit,
     includeSourceTags: options.sourceTags,
     embedText,
+    prefetchEmbedTexts,
     onProgress: options.quiet
       ? null
       : (event) => {