fix: extraction, multi-hop traversal, and aggregate result shape (BR-ADV-FEATURES-BUN)

Three advanced-API correctness fixes, all reproducible on Node (not Bun-specific):

- Entity/concept extraction returned []. SmartExtractor combined agreeing signals
  with a weighted sum compared against an absolute 0.60 gate, so a confident
  low-weight signal lost selection to a mediocre high-weight one that then failed
  the gate, dropping the whole result. Select and gate on a normalized weighted
  average instead. The public `confidence` option now controls the threshold (was
  a dead hardcoded 0.60), and the embedding-signal timeout is raised 100ms -> 2000ms
  so the neural signal is not silently dropped on slower runtimes.

- Multi-hop find({ connected }) returned only the 1-hop neighbour. executeGraphSearch
  ignored depth/via; it now delegates to the depth-aware neighbors() BFS.

- find({ aggregate }) hid groupKey/metrics/count under .metadata, so callers
  expecting AggregateResult saw empty rows. Expose those fields at the top level.

Adds real-embedding regression tests in tests/integration/advanced-apis-regression.test.ts.

RELEASES.md

@@ -11,6 +11,39 @@ Collective. The SDK wraps it — most products never call Brainy directly. Read
 
 ---
 
+## v7.22.1 — 2026-05-26
+
+**Affected products:** Anyone using `extractEntities()` / `extractConcepts()`, native
+aggregation (`find({ aggregate })`), or multi-hop graph traversal
+(`find({ connected: { depth } })`). Three advanced-API correctness fixes — all reproduce on
+Node, so they were **not** Bun-specific despite the original report (BR-ADV-FEATURES-BUN).
+
+### Entity/concept extraction no longer returns `[]`
+
+`extractEntities()` / `extractConcepts()` returned empty for entity-rich text. The SmartExtractor
+ensemble scored agreeing signals with a weighted **sum** against an absolute 0.60 gate, so a
+confident low-weight signal (e.g. a name pattern at 0.82) lost selection to a mediocre
+high-weight signal that then failed the gate — dropping the whole result. It now selects and
+gates on a normalized weighted **average**. Also: the `confidence` option now actually controls
+the threshold (was a dead hardcoded 0.60), and the embedding-signal timeout was raised
+100ms → 2000ms so the neural signal isn't silently dropped on slower runtimes.
+
+*Known limitation:* type accuracy on dense multi-entity sentences is still imperfect — a strong
+indicator in one entity's surrounding text can bleed onto neighbours. Tracked separately.
+
+### `find({ aggregate })` exposes `groupKey` / `metrics` / `count`
+
+Aggregation always computed correct values, but rows nested them under `.metadata`, so callers
+expecting the documented `AggregateResult` shape saw empty-looking rows. Those three fields are
+now also present at the top level of each result row.
+
+### Multi-hop `find({ connected: { depth } })` honours depth
+
+Previously returned only the immediate (1-hop) neighbour at any depth because the graph-search
+path ignored `depth` / `via`. It now performs the full depth-aware traversal.
+
+---
+
 ## v7.22.0 — 2026-05-15
 
 **Affected products:** All. Fixes a silent-data-loss class affecting `find()` and

src/brainy.ts

@@ -4559,7 +4559,7 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     }
   ): Promise<string[]> {
     const entities = await this.extract(text, {
-      types: [NounType.Concept, NounType.Concept],
+      types: [NounType.Concept],
       confidence: options?.confidence || 0.7,
       neuralMatching: true
     })
@@ -6787,36 +6787,58 @@ export class Brainy<T = any> implements BrainyInterface<T> {
   }
   
   /**
-   * Execute graph search component with O(1) traversal
+   * Execute graph search component.
+   *
+   * Honors the full `GraphConstraints` contract: multi-hop `depth` (breadth-first via
+   * `neighbors()`), `via`/`type` verb-type filtering, and `direction`. Previously this read
+   * only `from`/`to`/`direction` and did a single 1-hop `getNeighbors()`, so `depth` and `via`
+   * were silently ignored — `find({ connected: { from, depth: 3 } })` returned only the
+   * immediate neighbour at every depth.
    */
   private async executeGraphSearch(params: FindParams<T>, existingResults: Result<T>[]): Promise<Result<T>[]> {
     if (!params.connected) return existingResults
-    
+
-    const { from, to, direction = 'both' } = params.connected
+    const { from, to, depth, direction = 'both' } = params.connected
-    const connectedIds: string[] = []
+    const via = params.connected.via ?? params.connected.type
-    
+
+    // GraphConstraints speaks 'in' | 'out' | 'both'; neighbors() speaks 'incoming' | 'outgoing' | 'both'.
+    const toNeighborDir = (d: 'in' | 'out' | 'both'): 'incoming' | 'outgoing' | 'both' =>
+      d === 'in' ? 'incoming' : d === 'out' ? 'outgoing' : 'both'
+
+    // Collect reachable ids honoring depth + verb-type filter. `via` may be a single VerbType
+    // or an array; reuse the depth-aware BFS in neighbors() (one pass per requested verb type).
+    const collect = async (id: string, dir: 'incoming' | 'outgoing' | 'both'): Promise<string[]> => {
+      const verbTypes: Array<VerbType | undefined> =
+        via === undefined ? [undefined] : Array.isArray(via) ? via : [via]
+      const ids = new Set<string>()
+      for (const verbType of verbTypes) {
+        const hops = await this.neighbors(id, { direction: dir, depth: depth ?? 1, verbType })
+        for (const n of hops) ids.add(n)
+      }
+      return [...ids]
+    }
+
+    const connectedIds = new Set<string>()
+
     if (from) {
-      const neighbors = await this.graphIndex.getNeighbors(from, direction)
+      for (const id of await collect(from, toNeighborDir(direction))) connectedIds.add(id)
-      connectedIds.push(...neighbors)
     }
-    
+
     if (to) {
-      const reverseDirection = direction === 'in' ? 'out' : direction === 'out' ? 'in' : 'both'
+      const reverse: 'in' | 'out' | 'both' = direction === 'in' ? 'out' : direction === 'out' ? 'in' : 'both'
-      const neighbors = await this.graphIndex.getNeighbors(to, reverseDirection)
+      for (const id of await collect(to, toNeighborDir(reverse))) connectedIds.add(id)
-      connectedIds.push(...neighbors)
     }
-    
+
     // Filter existing results to only connected entities
     if (existingResults.length > 0) {
-      const connectedIdSet = new Set(connectedIds)
+      return existingResults.filter(r => connectedIds.has(r.id))
-      return existingResults.filter(r => connectedIdSet.has(r.id))
     }
-    
+
-    // Batch-load connected entities for 10x faster cloud storage performance
+    // Batch-load connected entities for fast cloud-storage performance
-    // GCS: 20 entities = 1×50ms vs 20×50ms = 1000ms (20x faster)
     const results: Result<T>[] = []
-    const entitiesMap = await this.batchGet(connectedIds)
+    const ids = [...connectedIds]
-    for (const id of connectedIds) {
+    const entitiesMap = await this.batchGet(ids)
+    for (const id of ids) {
       const entity = entitiesMap.get(id)
       if (entity) {
         results.push(this.createResult(id, 1.0, entity))
@@ -7912,7 +7934,14 @@ export class Brainy<T = any> implements BrainyInterface<T> {
         type: NounType.Measurement,
         metadata: entity.metadata,
         data: entity.data,
-        entity
+        entity,
+        // Surface the documented AggregateResult fields at the top level so consumers can read
+        // groupKey/metrics/count directly. Previously these were only reachable under .metadata,
+        // so callers expecting an AggregateResult saw rows with no groupKey/metrics/count and
+        // interpreted the output as degenerate/empty.
+        groupKey: agg.groupKey,
+        metrics: agg.metrics,
+        count: agg.count
       }
     })
   }

src/neural/SmartExtractor.ts

@@ -211,12 +211,18 @@ export class SmartExtractor {
       formatContext?: FormatContext
       allTerms?: string[]
       metadata?: any
-    }
+    },
+    minConfidence?: number
   ): Promise<ExtractionResult | null> {
     this.stats.calls++
 
+    // Per-call confidence threshold (falls back to the instance default). This lets callers
+    // such as brain.extractEntities({ confidence }) actually loosen or tighten the gate; it is
+    // part of the cache key below so results computed at one threshold are not reused at another.
+    const threshold = minConfidence ?? this.options.minConfidence
+
     // Check cache first
-    const cacheKey = this.getCacheKey(candidate, context)
+    const cacheKey = this.getCacheKey(candidate, context, threshold)
     const cached = this.getFromCache(cacheKey)
     if (cached !== undefined) {
       this.stats.cacheHits++
@@ -282,8 +288,8 @@ export class SmartExtractor {
 
       // Combine using ensemble or best signal
       const result = this.options.enableEnsemble
-        ? this.combineEnsemble(signalResults, formatHints, context?.formatContext)
+        ? this.combineEnsemble(signalResults, formatHints, context?.formatContext, threshold)
-        : this.selectBestSignal(signalResults, formatHints, context?.formatContext)
+        : this.selectBestSignal(signalResults, formatHints, context?.formatContext, threshold)
 
       // Cache result (including nulls to avoid recomputation)
       this.addToCache(cacheKey, result)
@@ -509,7 +515,8 @@ export class SmartExtractor {
   private combineEnsemble(
     signalResults: SignalResult[],
     formatHints: string[],
-    formatContext?: FormatContext
+    formatContext?: FormatContext,
+    minConfidence: number = this.options.minConfidence
   ): ExtractionResult | null {
     // Filter out null results
     const validResults = signalResults.filter(r => r.type !== null)
@@ -518,58 +525,63 @@ export class SmartExtractor {
       return null
     }
 
-    // Count votes by type with weighted confidence
+    // Group the signals by the type they voted for
-    const typeScores = new Map<NounType, { score: number; signals: SignalResult[] }>()
+    const typeScores = new Map<NounType, SignalResult[]>()
 
     for (const result of validResults) {
       if (!result.type) continue
 
-      const weighted = result.confidence * result.weight
       const existing = typeScores.get(result.type)
-
       if (existing) {
-        existing.score += weighted
+        existing.push(result)
-        existing.signals.push(result)
       } else {
-        typeScores.set(result.type, { score: weighted, signals: [result] })
+        typeScores.set(result.type, [result])
       }
     }
 
-    // Find best type
+    // Score each candidate type by a NORMALIZED, weighted-average confidence
+    // — Σ(confidence·weight) / Σ(weight of its signals) — plus a small boost when
+    // multiple signals concur, then select the type with the highest such confidence.
+    //
+    // Why an average and not a sum (this was the bug): a weighted *sum* lives on the
+    // signal-weight scale, so two signals agreeing on a fresh brain summed to ≈0.37 —
+    // below the 0.60 gate — meaning agreement was effectively *penalized*. Worse, selecting
+    // the best type by that sum let a high-weight signal with mediocre confidence
+    // (embedding @0.51 · 0.35 = 0.179) outrank a low-weight signal with high confidence
+    // (pattern @0.82 · 0.20 = 0.164); the wrong type won selection, then failed the threshold
+    // on its own 0.51 confidence, and the whole extraction returned null. Averaging keeps the
+    // score on the same 0–1 scale as the threshold, so selection and the gate agree and the
+    // most-confident type wins (pattern @0.82 here).
     let bestType: NounType | null = null
-    let bestScore = 0
+    let finalConfidence = 0
     let bestSignals: SignalResult[] = []
 
-    for (const [type, data] of typeScores.entries()) {
+    for (const [type, signals] of typeScores.entries()) {
-      // Apply agreement boost (multiple signals agree)
+      const weightSum = signals.reduce((sum, s) => sum + s.weight, 0)
-      let finalScore = data.score
+      const weightedConfidence = signals.reduce((sum, s) => sum + s.confidence * s.weight, 0)
-      if (data.signals.length > 1) {
+      let confidence = weightSum > 0 ? weightedConfidence / weightSum : 0
-        const agreementBoost = 0.05 * (data.signals.length - 1)
+
-        finalScore += agreementBoost
+      // Reward agreement between independent signals
-        this.stats.agreementBoosts++
+      if (signals.length > 1) {
+        confidence = Math.min(confidence + 0.05 * (signals.length - 1), 1.0)
       }
 
-      if (finalScore > bestScore) {
+      if (confidence > finalConfidence) {
-        bestScore = finalScore
+        finalConfidence = confidence
         bestType = type
-        bestSignals = data.signals
+        bestSignals = signals
       }
     }
 
-    // Determine final confidence score
-    // FIX: When only one signal matches, use its original confidence instead of weighted score
-    // The weighted score is too low when only one signal matches (e.g., 0.8 * 0.2 = 0.16 < 0.60 threshold)
-    let finalConfidence = bestScore
-    if (bestSignals.length === 1) {
-      // Single signal: use its original confidence
-      finalConfidence = bestSignals[0].confidence
-    }
-
     // Check minimum confidence threshold
-    if (!bestType || finalConfidence < this.options.minConfidence) {
+    if (!bestType || finalConfidence < minConfidence) {
       return null
     }
 
+    if (bestSignals.length > 1) {
+      this.stats.agreementBoosts++
+    }
+
     // Track signal contributions
     const usedSignals = bestSignals.length
     this.stats.averageSignalsUsed =
@@ -604,13 +616,17 @@ export class SmartExtractor {
   private selectBestSignal(
     signalResults: SignalResult[],
     formatHints: string[],
-    formatContext?: FormatContext
+    formatContext?: FormatContext,
+    minConfidence: number = this.options.minConfidence
   ): ExtractionResult | null {
-    // Filter valid results and sort by weighted confidence
+    // Select by confidence — the same metric the threshold checks below. Sorting by
+    // weighted score (confidence·weight) instead would let a high-weight, mediocre-confidence
+    // signal outrank a low-weight, high-confidence one and then fail the gate, dropping the
+    // result entirely (the same defect fixed in combineEnsemble). Weight matters for ensemble
+    // voting, not for picking the single best signal.
     const validResults = signalResults
       .filter(r => r.type !== null)
-      .map(r => ({ ...r, weightedScore: r.confidence * r.weight }))
+      .sort((a, b) => b.confidence - a.confidence)
-      .sort((a, b) => b.weightedScore - a.weightedScore)
 
     if (validResults.length === 0) {
       return null
@@ -618,9 +634,7 @@ export class SmartExtractor {
 
     const best = validResults[0]
 
-    // FIX: Use original confidence, not weighted score for threshold check
+    if (best.confidence < minConfidence) {
-    // Weighted score is for ranking signals, not for absolute threshold
-    if (best.confidence < this.options.minConfidence) {
       return null
     }
 
@@ -661,11 +675,12 @@ export class SmartExtractor {
   /**
    * Get cache key from candidate and context
    */
-  private getCacheKey(candidate: string, context?: any): string {
+  private getCacheKey(candidate: string, context?: any, minConfidence?: number): string {
     const normalized = candidate.toLowerCase().trim()
     const defSnippet = context?.definition?.substring(0, 50) || ''
     const format = context?.formatContext?.format || ''
-    return `${normalized}:${defSnippet}:${format}`
+    const threshold = minConfidence ?? this.options.minConfidence
+    return `${normalized}:${defSnippet}:${format}:${threshold}`
   }
 
   /**

src/neural/entityExtractor.ts

@@ -140,14 +140,17 @@ export class NeuralEntityExtractor {
     
     // Step 2: Classify each candidate using SmartExtractor
     for (const candidate of candidates) {
-      // Use SmartExtractor for unified neural + rule-based classification
+      // Use SmartExtractor for unified neural + rule-based classification.
+      // Pass the caller's threshold through so `confidence` actually controls the gate
+      // (previously SmartExtractor applied a hardcoded 0.60 floor, so a low confidence
+      // option had no loosening effect).
       const classification = await this.smartExtractor.extract(candidate.text, {
         definition: candidate.context,
         allTerms: [candidate.text, candidate.context]
-      })
+      }, minConfidence)
 
-      // Skip if SmartExtractor returns null (low confidence) or below threshold
+      // SmartExtractor already gates at minConfidence; this guards against null only.
-      if (!classification || classification.confidence < minConfidence) {
+      if (!classification) {
         continue
       }
 

src/neural/signals/EmbeddingSignal.ts

@@ -43,7 +43,10 @@ export interface EmbeddingSignalOptions {
   minConfidence?: number // Minimum confidence threshold (default: 0.60)
   checkGraph?: boolean   // Check against graph entities (default: true)
   checkHistory?: boolean // Check against historical data (default: true)
-  timeout?: number       // Max time in ms (default: 100)
+  timeout?: number       // Max time in ms (default: 2000). A real transformer embed of a single
+                         // candidate is commonly 50–200ms (cold/under load higher); the previous
+                         // 100ms default silently timed out, dropping the neural signal entirely —
+                         // which alone left concept extraction (no regex fallback) returning nothing.
   cacheSize?: number     // LRU cache size (default: 1000)
 }
 
@@ -110,7 +113,7 @@ export class EmbeddingSignal {
       minConfidence: options?.minConfidence ?? 0.60,
       checkGraph: options?.checkGraph ?? true,
       checkHistory: options?.checkHistory ?? true,
-      timeout: options?.timeout ?? 100,
+      timeout: options?.timeout ?? 2000,
       cacheSize: options?.cacheSize ?? 1000
     }
   }

src/types/brainy.types.ts

@@ -122,6 +122,14 @@ export interface Result<T = any> {
   semanticScore?: number      // Semantic similarity score (0-1)
   matchSource?: 'text' | 'semantic' | 'both'  // Where this result came from
   rrfScore?: number           // Raw RRF fusion score (for advanced ranking analysis)
+
+  // Aggregation: present only on rows returned by find({ aggregate }). These mirror the
+  // documented AggregateResult shape so callers can read group/metric data at the top level
+  // instead of digging into `metadata`. (The same values are also flattened into `metadata`
+  // for backward compatibility.)
+  groupKey?: Record<string, string | number>   // Group-by key values for this aggregate row
+  metrics?: Record<string, number>             // Computed metric values (sum/avg/min/max/count)
+  count?: number                               // Total entity count in this group
 }
 
 /**

tests/integration/advanced-apis-regression.test.ts

@@ -0,0 +1,131 @@
+/**
+ * Regression tests for the three advanced-API defects reported as BR-ADV-FEATURES-BUN:
+ *
+ *  1. Entity/concept extraction returned `[]` for entity-rich text. Root cause: the
+ *     SmartExtractor ensemble combined agreeing signals with a weighted *sum* compared against
+ *     an absolute gate, so a confident-but-low-weight signal was discarded whenever a
+ *     higher-weight signal voted a different (lower-confidence) type. Fixed by selecting and
+ *     gating on a normalized weighted *average*.
+ *  2. `find({ aggregate })` rows did not expose the documented AggregateResult fields
+ *     (`groupKey`/`metrics`/`count`) at the top level, so callers saw "empty" rows.
+ *  3. `find({ connected: { depth } })` ignored `depth`/`via` and returned only the 1-hop
+ *     neighbour. Fixed by delegating to the depth-aware `neighbors()` BFS.
+ *
+ * These run with REAL embeddings (no mocks) — the original failures were invisible under the
+ * zero-vector mock embeddings used by the unit suite.
+ */
+
+import { describe, it, expect, beforeAll, afterAll } from 'vitest'
+import { Brainy } from '../../src/brainy.js'
+import { NounType, VerbType } from '../../src/types/graphTypes.js'
+
+describe('BR-ADV-FEATURES-BUN regression', () => {
+  let brain: Brainy
+
+  beforeAll(async () => {
+    brain = new Brainy({ storage: { type: 'memory' } })
+    await brain.init()
+    // Warm the embedding model so the first real extraction isn't paying cold-start latency.
+    await brain.embed('warmup')
+  })
+
+  afterAll(async () => {
+    await brain.close()
+  })
+
+  describe('Bug 1 — entity extraction is not empty', () => {
+    const text = 'Sarah Chen founded Acme Corp in New York'
+
+    it('extractEntities returns the entity spans (was [])', async () => {
+      const entities = await brain.extractEntities(text, { confidence: 0.1 })
+      expect(entities.length).toBeGreaterThan(0)
+      const texts = entities.map(e => e.text)
+      // The candidate spans must be surfaced (type accuracy on dense sentences is a separate
+      // pre-existing concern; this guards the reported "returns nothing" regression).
+      expect(texts).toContain('Sarah Chen')
+      expect(texts).toContain('Acme Corp')
+    })
+
+    it('a confident single-signal candidate classifies correctly in isolation', async () => {
+      // "Acme Corp" carries a strong Organization indicator ("Corp") with no competing context.
+      const entities = await brain.extractEntities('Acme Corp', { confidence: 0.6 })
+      expect(entities.length).toBeGreaterThan(0)
+      expect(entities.some(e => e.type === NounType.Organization)).toBe(true)
+    })
+
+    it('the confidence option actually controls the gate (no longer dead)', async () => {
+      const loose = await brain.extractEntities(text, { confidence: 0.1 })
+      const strict = await brain.extractEntities(text, { confidence: 0.99 })
+      expect(loose.length).toBeGreaterThan(0)
+      // Previously confidence had no effect because SmartExtractor applied a hardcoded 0.60
+      // floor; a near-1.0 threshold must now admit no fewer results than a loose one, and here
+      // strictly fewer (nothing scores ≥ 0.99).
+      expect(strict.length).toBeLessThanOrEqual(loose.length)
+    })
+  })
+
+  describe('Bug 3 — multi-hop traversal honors depth', () => {
+    let a: string
+    let b: string
+    let c: string
+
+    beforeAll(async () => {
+      a = await brain.add({ data: 'graph node A', type: NounType.Concept, metadata: { name: 'A' } })
+      b = await brain.add({ data: 'graph node B', type: NounType.Concept, metadata: { name: 'B' } })
+      c = await brain.add({ data: 'graph node C', type: NounType.Concept, metadata: { name: 'C' } })
+      await brain.relate({ from: a, to: b, type: VerbType.RelatedTo })
+      await brain.relate({ from: b, to: c, type: VerbType.RelatedTo })
+    })
+
+    const names = (rows: Array<{ metadata?: any }>) =>
+      rows.map(r => r.metadata?.name).filter(Boolean).sort()
+
+    it('depth 1 returns only the immediate neighbour', async () => {
+      const rows = await brain.find({ connected: { from: a, depth: 1, direction: 'out' } })
+      expect(names(rows)).toEqual(['B'])
+    })
+
+    it('depth 2 reaches the second hop (was 1-hop only)', async () => {
+      const rows = await brain.find({ connected: { from: a, depth: 2, direction: 'out' } })
+      expect(names(rows)).toEqual(['B', 'C'])
+    })
+
+    it('depth 3 on a 2-edge chain still returns B and C', async () => {
+      const rows = await brain.find({ connected: { from: a, depth: 3, direction: 'out' } })
+      expect(names(rows)).toEqual(['B', 'C'])
+    })
+  })
+
+  describe('Bug 2 — find({ aggregate }) exposes AggregateResult fields', () => {
+    beforeAll(async () => {
+      brain.defineAggregate({
+        name: 'spend_by_cat',
+        source: { type: NounType.Event },
+        groupBy: ['category'],
+        metrics: { total: { op: 'sum', field: 'amount' }, count: { op: 'count' } }
+      })
+      await brain.add({ data: 'coffee', type: NounType.Event, metadata: { category: 'food', amount: 5 } })
+      await brain.add({ data: 'lunch', type: NounType.Event, metadata: { category: 'food', amount: 12 } })
+      await brain.add({ data: 'bus', type: NounType.Event, metadata: { category: 'transit', amount: 3 } })
+      await brain.flush()
+      await new Promise(r => setTimeout(r, 500)) // let materialization debounce settle
+    })
+
+    it('rows carry top-level groupKey / metrics / count (was hidden under metadata)', async () => {
+      const rows: any[] = await brain.find({ aggregate: 'spend_by_cat' })
+      expect(rows.length).toBe(2)
+
+      const food = rows.find(r => r.groupKey?.category === 'food')
+      const transit = rows.find(r => r.groupKey?.category === 'transit')
+
+      expect(food).toBeDefined()
+      expect(food.groupKey).toEqual({ category: 'food' })
+      expect(food.metrics.total).toBe(17)
+      expect(food.count).toBe(2)
+
+      expect(transit).toBeDefined()
+      expect(transit.metrics.total).toBe(3)
+      expect(transit.count).toBe(1)
+    })
+  })
+})

tests/unit/neural/NaturalLanguageProcessor.test.ts

@@ -118,10 +118,11 @@ describe('NaturalLanguageProcessor', () => {
       expect(extraction).toBeDefined()
       expect(Array.isArray(extraction)).toBe(true)
 
-      // Entity extraction uses neural matching with type embeddings
+      // This unit suite runs with mock (zero-vector) embeddings, so the neural embedding
-      // Extraction quality depends on text context and entity similarity to known types
+      // signal can't fire here — only the array shape is asserted. Real, non-empty extraction
-      // For simple text without rich context, extraction may return empty array
+      // is verified against actual embeddings in
-      // This is correct behavior - it's better to return nothing than false positives
+      // tests/integration/advanced-apis-regression.test.ts (BR-ADV-FEATURES-BUN). An empty
+      // result here reflects the mock embeddings, not expected production behaviour.
     })
     
     it('should extract topics and concepts', async () => {