Merge branch 'worktree-agent-ad5da57e47b822152' into overnight-jul31

recall@1 60% to 72%, answered 48% to 72%, latency 3x better. But false
recall went 1/5 to 5/5: e5 packs every score into a narrow high band, so
the 0.55 gate now admits everything. Left the gate alone as instructed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ

AGENTS.md

@@ -43,14 +43,17 @@ notes. Without it, the floor `HashEmbedder` is used — deterministic but weak
 (Russian recall rarely clears the confidence gate, many commands fall to
 "clarify").
 
-**Download the embedder** (ONNX, ~90 MB):
+**Download the embedder** (ONNX, ~120 MB):
 
 ```sh
 make download-embedder
 ```
 
-This fetches `paraphrase-multilingual-MiniLM-L12-v2` (384-dim, 12-layer,
+This fetches `multilingual-e5-small` (384-dim, 12-layer, Russian and English)
-supports 50+ languages including Russian) to `models/embedder/`.
+to `models/embedder/multilingual-e5-small/`. It is an asymmetric retrieval
+model: the code puts `query: ` in front of a question and `passage: ` in front
+of a stored note, which is how e5 was trained. The quantized file is the one
+that is downloaded, deployed and measured.
 
 **Also need ONNX Runtime** (`libonnxruntime.so`):
 
@@ -64,8 +67,8 @@ sudo cp onnxruntime-linux-x64-1.15.1/lib/libonnxruntime.so* /usr/local/lib/
 ```json
 "voice": {
   "embedder": {
-    "model_path": "models/embedder/model_quantized.onnx",
+    "model_path": "models/embedder/multilingual-e5-small/model_quantized.onnx",
-    "tokenizer_path": "models/embedder/tokenizer.json",
+    "tokenizer_path": "models/embedder/multilingual-e5-small/tokenizer.json",
     "lib_path": "/usr/local/lib/libonnxruntime.so"
   }
 }

Makefile

@@ -152,9 +152,13 @@ deps-piper:
 		-o /tmp/piper.tar.gz
 	tar -xzf /tmp/piper.tar.gz -C deps/
 
-EMBEDDER_DIR := $(shell pwd)/models/embedder
+# multilingual-e5-small: an asymmetric retrieval model. It is trained to match
-EMBEDDER_MODEL_URL := https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2/resolve/main/onnx/model_quantized.onnx
+# a short question against a longer passage, which is what note recall is.
-EMBEDDER_TOKENIZER_URL := https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2/resolve/main/tokenizer.json
+# The quantized file is the one we download, deploy and measure — see
+# RECALL-EVAL-31-07-2026.md.
+EMBEDDER_DIR := $(shell pwd)/models/embedder/multilingual-e5-small
+EMBEDDER_MODEL_URL := https://huggingface.co/Xenova/multilingual-e5-small/resolve/main/onnx/model_quantized.onnx
+EMBEDDER_TOKENIZER_URL := https://huggingface.co/Xenova/multilingual-e5-small/resolve/main/tokenizer.json
 
 download-embedder:
 	mkdir -p $(EMBEDDER_DIR)

RECALL-EVAL-31-07-2026.md

@@ -83,6 +83,66 @@ sqlite-backed `store.MemoryStore` and `memory.InMemoryStore` identically — bot
 (`internal/store/memory.go:64`) at ~150µs over 42 rows against a ~59ms query embed. An ANN index is
 not the problem to solve.
 
+## Re-measured after the embedder swap — 31-07-2026, later the same day
+
+Changed: `models/embedder/` is now **multilingual-e5-small** (quantized, 118MB), with `query: ` in
+front of a question and `passage: ` in front of a stored note (Vikunja #371). `deploy/mavend.json`
+and `make download-embedder` now name the same file, and it is the quantized one — that is what the
+column below measures (Vikunja #372). Everything else is unchanged: same fixture, same store, same
+0.55 gate. The old column is the baseline and is left as it was.
+
+| | recall+onnx, MiniLM (baseline) | recall+onnx, e5-small (new) |
+|---|---|---|
+| **recall@1** | 60.0% (15/25) | **72.0% (18/25)** |
+| recall@3 | 80.0% (20/25) | 84.0% (21/25) |
+| **answered after the 0.55 gate** | 48.0% (12/25) | **72.0% (18/25)** |
+| wrong note on top / tie on top | 10 / 0 | 7 / 0 |
+| ranked first, then silenced by the gate | 3 | 0 |
+| **false recall** | 1/5 (20%) | **5/5 (100%)** |
+| top-1 score when right, min / median | 0.559 / 0.678 | 0.791 / 0.857 |
+| top-1 when it must stay silent, median / max | 0.470 / 0.567 | 0.815 / 0.835 |
+| RU / EN / `hard` cases passed | 13/24 / 3/6 / 2/11 | 14/24 / 4/6 / 5/11 |
+| latency p50 / p95 / max | 59ms / 148ms / 194ms | 18ms / 37ms / 49ms |
+
+### What moved
+
+Ranking got better and got faster. Half the previously-unwinnable `hard` cases now pass (2/11 →
+5/11), the guitar note no longer beats the docker-logs note, and the gate stops silencing notes that
+already ranked first. The quantized e5 is also ~3x quicker than the fp32 MiniLM it replaces.
+
+### What got worse: the gate is now a no-op
+
+e5 packs every cosine into a narrow high band. Right-note scores start at 0.791; must-stay-silent
+scores reach 0.835. **The distributions still overlap, and now they overlap above the gate**, so
+0.55 admits everything and false recall goes from 1/5 to 5/5. The sweep:
+
+```
+gate 0.50–0.70: answered 18/25 (72%)  false recall 5/5
+gate 0.80:      answered 17/25 (68%)  false recall 4/5
+gate 0.90:      answered  0/25 ( 0%)  false recall 0/5
+```
+
+There is no value that keeps real recall and rejects made-up questions — same conclusion as before,
+now with a wider band and no room at all. `query_min_score` was left at 0.55 as instructed. **The
+recommendation is to leave it there and stop tuning it**: any number under ~0.79 is a no-op and
+anything above starts cutting real recall long before it stops the false ones. The fix is a margin
+gate (`top1 − top2 > δ`), next-steps item 3, which is now the top item.
+
+### The prefixes did not do the work
+
+A control run with both prefixes set to the empty string scored the **same** recall@1 (72%), a
+slightly better recall@3 (88%) and the same 5/5 false recall. So on this fixture the gain comes from
+the model, not from the `query:` / `passage:` split. The prefixes are kept because they are how e5
+was trained and the split is the right shape for the read path, but they are not worth defending on
+this evidence — a bigger fixture may say otherwise.
+
+### Stored vectors from the old model are now junk
+
+Cosine between a MiniLM vector and an e5 vector means nothing. Every row already in `notes` and in
+the vector memory table was written by the old model, so after this deploy they will score as noise
+against a new query. A live database needs every note and fact re-embedded before recall works at
+all. Filed as its own task.
+
 ## Next steps — ordered by value-to-risk; nothing here is a decision
 
 1. **Swap the embedder to `multilingual-e5-small` with `query:`/`passage:` prefixes.** One config

cmd/mavend/voice.go

@@ -559,7 +559,7 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
 		// fail the fact write). Facts aren't in the notes table, so this is the
 		// only recall path for them — "когда я пил воду?" reads back from here.
 		if h.memStore != nil {
-			if vec, err := h.embedder.Embed(ctx, dec.Utterance); err != nil {
+			if vec, err := router.EmbedPassage(ctx, h.embedder, dec.Utterance); err != nil {
 				log.Printf("voice: embed fact for memory: %v", err)
 			} else if err := h.memStore.Insert(ctx, "fact:"+dec.Slots.Key+":"+strconv.FormatInt(now.Unix(), 10), vec, map[string]string{
 				"source": "voice",
@@ -673,7 +673,7 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
 		// embed the note text with the same model the classifier uses, persist
 		// via CoreAPI (source=tap:voice). Semantic recall lives in `notes`, not
 		// facts — no predicate reads it (spec's two-memory split).
-		vec, err := h.embedder.Embed(ctx, dec.Utterance)
+		vec, err := router.EmbedPassage(ctx, h.embedder, dec.Utterance)
 		if err != nil {
 			log.Printf("voice: embed note: %v", err)
 			return "не получилось сохранить заметку."
@@ -750,7 +750,7 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
 			return fmt.Sprintf("в %s сейчас %.0f градусов, %s.", w.Location, w.Temperature, w.Condition)
 		}
 
-		vec, err := h.embedder.Embed(ctx, dec.Utterance)
+		vec, err := router.EmbedQuery(ctx, h.embedder, dec.Utterance)
 		if err != nil {
 			log.Printf("voice: embed query: %v", err)
 			return "не получилось найти ответ."

deploy/mavend.json

@@ -36,8 +36,8 @@
     "stt": { "socket": "/run/maven/stt.sock", "lang": "ru" },
     "tts": { "socket": "/run/maven/tts.sock", "lang": "ru" },
     "embedder": {
-      "model_path": "/opt/maven/models/embedder/model.onnx",
+      "model_path": "/opt/maven/models/embedder/multilingual-e5-small/model_quantized.onnx",
-      "tokenizer_path": "/opt/maven/models/embedder/tokenizer.json",
+      "tokenizer_path": "/opt/maven/models/embedder/multilingual-e5-small/tokenizer.json",
       "lib_path": "/opt/maven/lib/libonnxruntime.so"
     },
     "llm_router": false,

internal/config/config.go

@@ -262,13 +262,13 @@ type VoiceConfig struct {
 	// the model gets 50.0% of intents right against the classifier's 36.8%, but
 	// it costs about 800ms per turn instead of 30ms.
 	//
-	// TODO: the default stays false until two things land.
+	// TODO: the default stays false until this lands.
-	//   1. The LLM router cannot refuse. LLMRouter.Route hardcodes
+	// Extractor.Extract never runs on an LLM decision, so acts arrive with no
-	//      Confidence: 1.0, so the stage-3 clarify gate never fires and an
+	// Fn and reminders with no Time. Turning this on today makes routing more
-	//      unclear utterance becomes a confident wrong action (Vikunja #359).
+	// accurate and less safe.
-	//   2. Extractor.Extract never runs on an LLM decision, so acts arrive with
+	//
-	//      no Fn and reminders with no Time.
+	// The router can now refuse: it answers "unknown" when it cannot route, and
-	// Turning this on today makes routing more accurate and less safe.
+	// the turn drops to the classifier and its clarify gate (Vikunja #359).
 	LLMRouter bool `json:"llm_router,omitempty"`
 
 	// QueryMinScore — the note-recall confidence gate. Top cosine below this

internal/memory/recalleval/recalleval.go

@@ -117,18 +117,40 @@ type cachingEmbedder struct {
 	seen  map[string][]float32
 }
 
+var _ router.AsymmetricEmbedder = (*cachingEmbedder)(nil)
+
 func (c *cachingEmbedder) Dim() int     { return c.inner.Dim() }
 func (c *cachingEmbedder) Close() error { return nil } // the caller owns inner
 
 func (c *cachingEmbedder) Embed(ctx context.Context, text string) ([]float32, error) {
-	if v, ok := c.seen[text]; ok {
+	return c.cached(ctx, "embed:"+text, func() ([]float32, error) {
+		return c.inner.Embed(ctx, text)
+	})
+}
+
+// The two sides of an asymmetric embedder give different vectors for the same
+// string, so the cache key has to say which side asked.
+func (c *cachingEmbedder) EmbedQuery(ctx context.Context, text string) ([]float32, error) {
+	return c.cached(ctx, "query:"+text, func() ([]float32, error) {
+		return router.EmbedQuery(ctx, c.inner, text)
+	})
+}
+
+func (c *cachingEmbedder) EmbedPassage(ctx context.Context, text string) ([]float32, error) {
+	return c.cached(ctx, "passage:"+text, func() ([]float32, error) {
+		return router.EmbedPassage(ctx, c.inner, text)
+	})
+}
+
+func (c *cachingEmbedder) cached(_ context.Context, key string, embed func() ([]float32, error)) ([]float32, error) {
+	if v, ok := c.seen[key]; ok {
 		return v, nil
 	}
-	v, err := c.inner.Embed(ctx, text)
+	v, err := embed()
 	if err != nil {
 		return nil, err
 	}
-	c.seen[text] = v
+	c.seen[key] = v
 	return v, nil
 }
 
@@ -305,7 +327,7 @@ func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minS
 
 	all := append(append([]StoredNote(nil), c.Notes...), filler...)
 	for _, n := range all {
-		vec, err := emb.Embed(ctx, n.Text)
+		vec, err := router.EmbedPassage(ctx, emb, n.Text)
 		if err != nil {
 			return Outcome{}, fmt.Errorf("%s: embed note %s: %w", c.ID, n.ID, err)
 		}
@@ -317,7 +339,7 @@ func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minS
 
 	o := Outcome{Case: c}
 	start := time.Now()
-	qvec, err := emb.Embed(ctx, c.Query)
+	qvec, err := router.EmbedQuery(ctx, emb, c.Query)
 	if err != nil {
 		o.Latency = time.Since(start)
 		o.Err = err

internal/memory/recalleval/recalleval_test.go

@@ -246,8 +246,8 @@ func TestONNXRecall(t *testing.T) {
 	if lib == "" {
 		t.Skip("MAVEN_ONNX_LIB unset — see AGENTS.md § Embedder model for intent routing")
 	}
-	model := filepath.Join("../../..", "models/embedder/model.onnx")
+	model := filepath.Join("../../..", "models/embedder/multilingual-e5-small/model_quantized.onnx")
-	tok := filepath.Join("../../..", "models/embedder/tokenizer.json")
+	tok := filepath.Join("../../..", "models/embedder/multilingual-e5-small/tokenizer.json")
 	for _, p := range []string{lib, model, tok} {
 		if _, err := os.Stat(p); err != nil {
 			t.Skipf("missing %s: %v", p, err)

internal/router/embedder.go

@@ -19,6 +19,37 @@ type Embedder interface {
 	Close() error
 }
 
+// AsymmetricEmbedder — an embedder that wants to know whether a text is a
+// search query or a stored passage. Recall is asymmetric: a short question
+// goes in, a longer note comes out. The e5 family is trained for exactly that
+// and needs the side written into the text ("query: " / "passage: ").
+//
+// Optional on purpose: HashEmbedder has no such notion, so callers go through
+// EmbedQuery and EmbedPassage below, which fall back to plain Embed.
+type AsymmetricEmbedder interface {
+	Embedder
+	EmbedQuery(ctx context.Context, text string) ([]float32, error)
+	EmbedPassage(ctx context.Context, text string) ([]float32, error)
+}
+
+// EmbedQuery embeds text that is being searched WITH — a question.
+func EmbedQuery(ctx context.Context, e Embedder, text string) ([]float32, error) {
+	if a, ok := e.(AsymmetricEmbedder); ok {
+		return a.EmbedQuery(ctx, text)
+	}
+	return e.Embed(ctx, text)
+}
+
+// EmbedPassage embeds text that is being searched FOR — a note or a fact on
+// its way into the store. Store and lookup must use these two calls, not one
+// of them twice, or the asymmetry buys nothing.
+func EmbedPassage(ctx context.Context, e Embedder, text string) ([]float32, error) {
+	if a, ok := e.(AsymmetricEmbedder); ok {
+		return a.EmbedPassage(ctx, text)
+	}
+	return e.Embed(ctx, text)
+}
+
 // HashEmbedder — a deterministic bag-of-words embedder used for tests and as a
 // non-zero default floor. NOT semantically meaningful across languages; the
 // real classifier swaps in the multilingual ONNX model wholesale.

internal/router/embedder_test.go

@@ -37,3 +37,60 @@ func TestHashEmbedderCyrillic(t *testing.T) {
 		t.Fatalf("cosine(shared)=%.3f not > cosine(disjoint)=%.3f", cosine(a, b), cosine(a, c))
 	}
 }
+
+// recordingEmbedder — an asymmetric embedder that only remembers which side
+// was asked for. Enough to pin the dispatch; real vectors need the model.
+type recordingEmbedder struct{ calls []string }
+
+func (r *recordingEmbedder) Dim() int     { return 2 }
+func (r *recordingEmbedder) Close() error { return nil }
+
+func (r *recordingEmbedder) Embed(_ context.Context, _ string) ([]float32, error) {
+	r.calls = append(r.calls, "embed")
+	return []float32{1, 0}, nil
+}
+
+func (r *recordingEmbedder) EmbedQuery(_ context.Context, _ string) ([]float32, error) {
+	r.calls = append(r.calls, "query")
+	return []float32{1, 0}, nil
+}
+
+func (r *recordingEmbedder) EmbedPassage(_ context.Context, _ string) ([]float32, error) {
+	r.calls = append(r.calls, "passage")
+	return []float32{0, 1}, nil
+}
+
+// TestEmbedQueryAndPassageSplit — a question and a stored note must not take
+// the same path. If both ended up on the same call the asymmetric model buys
+// nothing, which is the whole reason for the swap.
+func TestEmbedQueryAndPassageSplit(t *testing.T) {
+	rec := &recordingEmbedder{}
+	if _, err := EmbedQuery(context.Background(), rec, "где логи?"); err != nil {
+		t.Fatalf("EmbedQuery: %v", err)
+	}
+	if _, err := EmbedPassage(context.Background(), rec, "логи в /var/log"); err != nil {
+		t.Fatalf("EmbedPassage: %v", err)
+	}
+	if len(rec.calls) != 2 || rec.calls[0] != "query" || rec.calls[1] != "passage" {
+		t.Errorf("calls %v, want [query passage]", rec.calls)
+	}
+}
+
+// TestEmbedFallsBackToPlainEmbed — HashEmbedder has no sides, so both helpers
+// must still work and give the same vector.
+func TestEmbedFallsBackToPlainEmbed(t *testing.T) {
+	h := NewHashEmbedder(64)
+	q, err := EmbedQuery(context.Background(), h, "text")
+	if err != nil {
+		t.Fatalf("EmbedQuery: %v", err)
+	}
+	p, err := EmbedPassage(context.Background(), h, "text")
+	if err != nil {
+		t.Fatalf("EmbedPassage: %v", err)
+	}
+	for i := range q {
+		if q[i] != p[i] {
+			t.Fatalf("hash embedder gave two different vectors for the same text")
+		}
+	}
+}

internal/router/eval/eval_test.go

@@ -185,8 +185,8 @@ func TestONNXBaseline(t *testing.T) {
 	if lib == "" {
 		t.Skip("MAVEN_ONNX_LIB unset — see AGENTS.md § Embedder model for intent routing")
 	}
-	model := filepath.Join("../../..", "models/embedder/model.onnx")
+	model := filepath.Join("../../..", "models/embedder/multilingual-e5-small/model_quantized.onnx")
-	tok := filepath.Join("../../..", "models/embedder/tokenizer.json")
+	tok := filepath.Join("../../..", "models/embedder/multilingual-e5-small/tokenizer.json")
 	for _, p := range []string{lib, model, tok} {
 		if _, err := os.Stat(p); err != nil {
 			t.Skipf("missing %s: %v", p, err)

internal/router/llmrouter.go

@@ -29,7 +29,7 @@ func NewLLMRouter(c Completer) *LLMRouter { return &LLMRouter{c: c} }
 const routeGrammar = `
 root   ::= "[" ws action ("," ws action)* ws "]"
 action ::= "{" ws "\"intent\"" ws ":" ws intent ("," ws field)* ws "}"
-intent ::= "\"fact\"" | "\"reminder\"" | "\"note\"" | "\"query\"" | "\"act\"" | "\"chat\"" | "\"system\""
+intent ::= "\"fact\"" | "\"reminder\"" | "\"note\"" | "\"query\"" | "\"act\"" | "\"chat\"" | "\"system\"" | "\"unknown\""
 field  ::= key ws ":" ws string
 key    ::= "\"key\"" | "\"value\"" | "\"text\"" | "\"verb\""
 string ::= "\"" ([^"\\] | "\\" .){0,120} "\""
@@ -41,12 +41,17 @@ ws     ::= [ \t\n]*
 // question naming a fact key ("сколько воды я выпил с утра") matched the fact
 // rule first and was stored as an assertion — 15 of 76 fixture cases.
 //
+// Changed again 31-07-2026: added the "unknown" escape hatch so the model can
+// admit it cannot route (Vikunja #359).
+//
 // The training workspace keeps its own copy of this prompt for relabelling, and
 // `llm/check_prompt_parity.py` there compares the two. That copy is in another
-// repo and was not touched, so parity will fail until it gets the same edit.
+// repo and was not touched, so parity will fail until it gets the same edits —
+// both the rule reorder and the "unknown" wording (Vikunja #362).
 const routeSystem = `Классифицируй ровно одно сообщение пользователя. Верни ОДИН JSON-массив действий.
 
 Ровно одно намерение: fact, reminder, note, query, act, chat, system.
+Есть восьмое значение unknown — только для случаев, когда просьбу невозможно понять.
 
 Классифицируй по цели пользователя. Порядок решения:
 1. Хочет напоминание в будущем → reminder
@@ -56,12 +61,14 @@ const routeSystem = `Классифицируй ровно одно сообще
 5. Утверждает: сообщает или обновляет текущее состояние/событие → fact
 6. Просит выполнить работу → act
 7. Про ассистента, настройки или память → system
-8. Иначе → chat
+8. Реплика — обрывок или указание на неназванное («это», «то», «потом»), и без него непонятно, что именно нужно сделать → unknown
+9. Иначе → chat
 
 Различия:
 - note — сохранить информацию, без напоминания. text = суть.
 - reminder — уведомить позже. text = что напомнить.
 - fact — неявное обновление: пользователь сообщает, что что-то в мире изменилось (текущее/изменённое состояние, случившееся событие). key/value.
+- unknown — редкий случай. Ставь его, только если в самой реплике нет ни предмета, ни действия. Короткая, простая или незнакомая тема — это не причина для unknown: приветствие и болтовня — это chat, вопрос на любую тему — это query, просьба сделать что-то названное — это act.
 - query против fact — решает форма реплики, а не тема. Вопрос о состоянии — это query, даже если названо то же самое, что бывает в fact. Только утверждение — это fact.
 
 Примеры:
@@ -76,6 +83,13 @@ const routeSystem = `Классифицируй ровно одно сообще
 "напиши письмо" → {"intent":"act","verb":"написать письмо"}
 "очисти память" → {"intent":"system"}
 "привет" → {"intent":"chat","text":"привет"}
+"сделай это" → {"intent":"unknown"}
+"ну это" → {"intent":"unknown"}
+"потом" → {"intent":"unknown"}
+Но не путай — здесь unknown не нужен:
+"сделай кофе" → {"intent":"act","verb":"сделать кофе"}
+"что такое кватернион?" → {"intent":"query","text":"что такое кватернион"}
+"ага" → {"intent":"chat","text":"ага"}
 
 Ответ — JSON-массив: по одному объекту на каждую просьбу. Обычно один. Если в реплике несколько просьб — по объекту на каждую. "напомни купить молоко, и запиши что кофе кончился" → [{"intent":"reminder","text":"купить молоко"},{"intent":"note","text":"кофе кончился"}]. Только JSON, без пояснений.`
 
@@ -84,6 +98,11 @@ const routeSystem = `Классифицируй ровно одно сообще
 // the loop without hurting short slot values.
 const routeRepeatPenalty = 1.15
 
+// routeIntentUnknown — the model's way of saying "I could not route this".
+// It is a wire value only: it never becomes a router.Intent, it just makes
+// Route return ok=false so the caller drops to the classifier cascade.
+const routeIntentUnknown = "unknown"
+
 type routeAction struct {
 	Intent string `json:"intent"`
 	Key    string `json:"key"`
@@ -92,6 +111,10 @@ type routeAction struct {
 	Verb   string `json:"verb"`
 }
 
+// Route asks the model for one decision. The bool is false when there is no
+// decision to use: either the model failed (err set) or it refused with the
+// "unknown" intent (err nil). Both mean the same thing to the caller — use the
+// classifier instead.
 func (lr *LLMRouter) Route(ctx context.Context, utterance string, now time.Time) (Decision, bool, error) {
 	raw, err := lr.c.Complete(ctx, llm.Req{
 		System:        routeSystem,
@@ -115,6 +138,13 @@ func (lr *LLMRouter) Route(ctx context.Context, utterance string, now time.Time)
 	// with the engine turn-on (Router.Route → []Decision, both voice.go handlers
 	// loop). Until then only the first ask is honored.
 	a := acts[0]
+	// The model refused. Report "no decision" without an error, which is the
+	// same fall-through the caller already uses for a parse failure — the
+	// classifier cascade gets the turn and its own confidence gate decides
+	// whether to ask. Better a slower second opinion than a confident guess.
+	if a.Intent == routeIntentUnknown {
+		return Decision{}, false, nil
+	}
 	d := Decision{Utterance: utterance, Stage: 1, Confidence: 1.0}
 	switch Intent(a.Intent) {
 	case IntentFact:

internal/router/llmrouter_test.go

@@ -100,8 +100,10 @@ func TestLLMRouterReminderMapping(t *testing.T) {
 	}
 }
 
+// An intent name that is not in the contract at all (as opposed to "unknown",
+// which is a real refusal) still defaults to chat.
 func TestLLMRouterChatFallback(t *testing.T) {
-	lr := NewLLMRouter(mockLLM{out: `{"intent":"unknown"}`})
+	lr := NewLLMRouter(mockLLM{out: `{"intent":"banana"}`})
 	d, ok, err := lr.Route(context.Background(), "как дела?", time.Now())
 	if err != nil || !ok {
 		t.Fatalf("ok=%v err=%v", ok, err)
@@ -111,6 +113,61 @@ func TestLLMRouterChatFallback(t *testing.T) {
 	}
 }
 
+// The model must be able to say "I could not route this".
+func TestRouteGrammarAllowsUnknown(t *testing.T) {
+	if !strings.Contains(routeGrammar, `"\"unknown\""`) {
+		t.Fatal("grammar cannot express a refusal")
+	}
+}
+
+// If the prompt does not tell the model when to refuse, it never will.
+func TestRoutePromptExplainsUnknown(t *testing.T) {
+	if !strings.Contains(routeSystem, "unknown") {
+		t.Fatal("prompt never mentions the unknown intent")
+	}
+	if !strings.Contains(routeSystem, `"сделай это" → {"intent":"unknown"}`) {
+		t.Fatal("prompt lost its worked refusal example")
+	}
+	// A refusal-only router is useless, so the prompt must also show cases that
+	// look ambiguous but are not.
+	if !strings.Contains(routeSystem, "здесь unknown не нужен") {
+		t.Fatal("prompt lost its counter-examples")
+	}
+}
+
+// A refusal is not an error. It reports "no decision" so the cascade moves on.
+func TestLLMRouterUnknownRefuses(t *testing.T) {
+	lr := NewLLMRouter(mockLLM{out: `{"intent":"unknown"}`})
+	_, ok, err := lr.Route(context.Background(), "сделай это", time.Now())
+	if ok {
+		t.Fatal("a refusal must not produce a usable decision")
+	}
+	if err != nil {
+		t.Fatalf("a refusal is not an error, got %v", err)
+	}
+}
+
+// The whole point of the refusal: the turn keeps going on the classifier, the
+// same way it does when the model returns garbage.
+func TestRouterFallsBackWhenLLMRefuses(t *testing.T) {
+	c := NewClassifier(NewHashEmbedder(1024))
+	seedClassifier(t, c)
+	r := New(Config{
+		Classifier: c,
+		Extractor:  Extractor{Time: StubDateTimeParser{}, Facts: DefaultFactParser{}},
+		Threshold:  0.4,
+		LLM:        NewLLMRouter(mockLLM{out: `{"intent":"unknown"}`}),
+	})
+	d, err := r.Route(context.Background(), "напомни позвонить маме", refNow())
+	if err != nil {
+		t.Fatalf("route: %v", err)
+	}
+	// Stage 1 is the LLM's own answer; the classifier lands on stage 2 or 3.
+	if d.Stage < 2 {
+		t.Fatalf("want the classifier to decide, got stage %d (%+v)", d.Stage, d)
+	}
+}
+
 func TestLLMRouterLLMError(t *testing.T) {
 	lr := NewLLMRouter(mockLLM{out: "", err: fmt.Errorf("llm down")})
 	_, ok, err := lr.Route(context.Background(), "x", time.Now())

internal/router/onnxembedder.go

@@ -12,6 +12,15 @@ import (
 	"golang.org/x/text/unicode/norm"
 )
 
+// The deployed model is multilingual-e5-small. e5 was trained with these two
+// words glued to the front of every text, and it scores badly without them —
+// they are part of the model, not a style choice. Swapping back to a symmetric
+// paraphrase model means dropping them again.
+const (
+	queryPrefix   = "query: "
+	passagePrefix = "passage: "
+)
+
 const (
 	padTokenID = 1
 	unkTokenID = 3
@@ -54,7 +63,25 @@ func NewONNXEmbedder(modelPath, tokenizerPath, libPath string) (*onnxEmbedder, e
 
 func (e *onnxEmbedder) Dim() int { return embedDim }
 
+// Embed treats the text as a query. The classifier compares one short
+// utterance to another short seed phrase, so both sides get the same prefix
+// and the comparison stays fair. The recall path must call EmbedQuery and
+// EmbedPassage instead.
 func (e *onnxEmbedder) Embed(ctx context.Context, text string) ([]float32, error) {
+	return e.embed(ctx, queryPrefix+text)
+}
+
+// EmbedQuery — the question the user just asked.
+func (e *onnxEmbedder) EmbedQuery(ctx context.Context, text string) ([]float32, error) {
+	return e.embed(ctx, queryPrefix+text)
+}
+
+// EmbedPassage — a note or fact being stored, or re-scored at lookup time.
+func (e *onnxEmbedder) EmbedPassage(ctx context.Context, text string) ([]float32, error) {
+	return e.embed(ctx, passagePrefix+text)
+}
+
+func (e *onnxEmbedder) embed(ctx context.Context, text string) ([]float32, error) {
 	inputIDs, attentionMask, _ := e.tokenizer.Encode(text)
 
 	inputShape := ort.NewShape(1, int64(maxLength))
@@ -313,4 +340,4 @@ func preTokenize(text string) []string {
 	return out
 }
 
-var _ Embedder = (*onnxEmbedder)(nil)
+var _ AsymmetricEmbedder = (*onnxEmbedder)(nil)