fix: cancel abandoned highlight() semantic work and harden WASM engine recovery

highlight() used Promise.race with a 10s timeout, but the losing
semantic phase promise continued running 25 WASM micro-batches,
saturating the event loop and degrading all subsequent operations
(find() going from ~200ms to ~10,000ms).

Add AbortController to highlight() so the semantic phase stops
immediately on timeout or error. Pass abort signal through
embedBatch() → EmbeddingManager → micro-batch loop.

Also add defensive hardening:
- CandleEmbeddingEngine: try/catch around WASM calls resets engine
  state on failure so next call triggers re-initialization
- WASMEmbeddingEngine: initialize() now checks underlying Candle
  engine state, not just its own flag, completing the recovery chain

src/brainy.ts

@@ -4647,7 +4647,7 @@ export class Brainy<T = any> implements BrainyInterface<T> {
    * // embeddings.length === 3
    * // embeddings[0].length === 384
    */
-  async embedBatch(texts: string[]): Promise<number[][]> {
+  async embedBatch(texts: string[], options?: { signal?: AbortSignal }): Promise<number[][]> {
     await this.ensureInitialized()
 
     if (texts.length === 0) {
@@ -4655,7 +4655,7 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     }
 
     // Use native WASM batch API: single forward pass instead of N individual calls
-    return await embeddingManager.embedBatch(texts)
+    return await embeddingManager.embedBatch(texts, options)
   }
 
   /**
@@ -4784,20 +4784,25 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     }
 
     // === PHASE 2: Find semantic matches with timeout fallback ===
+    // AbortController ensures the background semantic work (WASM batch embedding)
+    // is cancelled on timeout or error, preventing event loop saturation and
+    // WASM engine crashes from abandoned promises.
     const SEMANTIC_TIMEOUT_MS = 10_000
+    const abortController = new AbortController()
+
     try {
       const semanticResult = await Promise.race([
-        this.highlightSemanticPhase(query, chunks, threshold, highlightMap),
+        this.highlightSemanticPhase(query, chunks, threshold, highlightMap, abortController.signal),
         new Promise<'timeout'>((resolve) => setTimeout(() => resolve('timeout'), SEMANTIC_TIMEOUT_MS))
       ])
 
       if (semanticResult === 'timeout') {
-        // Return Phase 1 text-only results on timeout
+        abortController.abort()
         const textHighlights = Array.from(highlightMap.values())
         return textHighlights.sort((a, b) => b.score - a.score)
       }
     } catch {
-      // On any error in semantic phase, return Phase 1 results
+      abortController.abort()
       const textHighlights = Array.from(highlightMap.values())
       return textHighlights.sort((a, b) => b.score - a.score)
     }
@@ -4815,14 +4820,19 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     query: string,
     chunks: Array<{ text: string, position: [number, number], contentCategory?: import('./types/brainy.types.js').ContentCategory }>,
     threshold: number,
-    highlightMap: Map<string, import('./types/brainy.types.js').Highlight>
+    highlightMap: Map<string, import('./types/brainy.types.js').Highlight>,
+    signal?: AbortSignal
   ): Promise<void> {
+    if (signal?.aborted) return
+
     // Get query embedding
     const queryVector = await this.embed(query)
+    if (signal?.aborted) return
 
     // Batch embed all chunks using native WASM batch API
     const chunkTexts = chunks.map(c => c.text)
-    const chunkVectors = await this.embedBatch(chunkTexts)
+    const chunkVectors = await this.embedBatch(chunkTexts, { signal })
+    if (signal?.aborted) return
 
     // Calculate semantic similarities
     for (let i = 0; i < chunks.length; i++) {

src/embeddings/EmbeddingManager.ts

@@ -219,7 +219,7 @@ export class EmbeddingManager {
    * @param texts Array of strings to embed
    * @returns Array of embedding vectors (384 dimensions each)
    */
-  async embedBatch(texts: string[]): Promise<number[][]> {
+  async embedBatch(texts: string[], options?: { signal?: AbortSignal }): Promise<number[][]> {
     if (texts.length === 0) return []
 
     const isTestMode =
@@ -248,6 +248,10 @@ export class EmbeddingManager {
     // so other requests (HTTP, timers, I/O) can proceed
     const allResults: number[][] = []
     for (let i = 0; i < texts.length; i += MICRO_BATCH_SIZE) {
+      if (options?.signal?.aborted) {
+        return allResults
+      }
+
       const batch = texts.slice(i, i + MICRO_BATCH_SIZE)
       const batchResults = await this.engine.embedBatch(batch)
       allResults.push(...batchResults)

src/embeddings/wasm/CandleEmbeddingEngine.ts

@@ -205,19 +205,27 @@ export class CandleEmbeddingEngine {
       throw new Error('Engine not properly initialized')
     }
 
-    const startTime = Date.now()
+    try {
+      const startTime = Date.now()
 
-    const embedding = this.engine.embed(text)
-    const embeddingArray = Array.from(embedding)
+      const embedding = this.engine.embed(text)
+      const embeddingArray = Array.from(embedding)
 
-    const processingTimeMs = Date.now() - startTime
-    this.embedCount++
-    this.totalProcessingTimeMs += processingTimeMs
+      const processingTimeMs = Date.now() - startTime
+      this.embedCount++
+      this.totalProcessingTimeMs += processingTimeMs
 
-    return {
-      embedding: embeddingArray,
-      tokenCount: 0, // Candle handles tokenization internally
-      processingTimeMs,
+      return {
+        embedding: embeddingArray,
+        tokenCount: 0, // Candle handles tokenization internally
+        processingTimeMs,
+      }
+    } catch (error) {
+      console.error('WASM embed failed, marking engine for re-initialization:', error)
+      this.initialized = false
+      this.engine = null
+      this.wasmModule = null
+      throw error
     }
   }
 
@@ -237,10 +245,18 @@ export class CandleEmbeddingEngine {
       return []
     }
 
-    const embeddings = this.engine.embed_batch(texts)
-    this.embedCount += texts.length
+    try {
+      const embeddings = this.engine.embed_batch(texts)
+      this.embedCount += texts.length
 
-    return embeddings.map((e) => Array.from(e))
+      return embeddings.map((e) => Array.from(e))
+    } catch (error) {
+      console.error('WASM embedBatch failed, marking engine for re-initialization:', error)
+      this.initialized = false
+      this.engine = null
+      this.wasmModule = null
+      throw error
+    }
   }
 
   /**

src/embeddings/wasm/WASMEmbeddingEngine.ts

@@ -52,8 +52,9 @@ export class WASMEmbeddingEngine {
    * Initialize the engine
    */
   async initialize(): Promise<void> {
-    // Already initialized
-    if (this.initialized) {
+    // Only skip if BOTH this layer and the underlying Candle engine are initialized.
+    // If Candle crashed and reset itself, we must re-initialize even though our flag is true.
+    if (this.initialized && this.candleEngine.isInitialized()) {
       return
     }