Merge branch 'worktree-agent-a76ce40c73601d90d' into overnight-jul31

An LLM-routed reminder came back with no parsed time and an act with no
fn, because only the classifier path ran the extractor. Now the router
runs the same extraction after an LLM decision and fills only the empty
slots. No time in the utterance still means no time.

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,
-supports 50+ languages including Russian) to `models/embedder/`.
+This fetches `multilingual-e5-small` (384-dim, 12-layer, Russian and English)
+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",
-    "tokenizer_path": "models/embedder/tokenizer.json",
+    "model_path": "models/embedder/multilingual-e5-small/model_quantized.onnx",
+    "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
-EMBEDDER_MODEL_URL := https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2/resolve/main/onnx/model_quantized.onnx
-EMBEDDER_TOKENIZER_URL := https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2/resolve/main/tokenizer.json
+# multilingual-e5-small: an asymmetric retrieval model. It is trained to match
+# a short question against a longer passage, which is what note recall is.
+# 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

ROUTING-EVAL-31-07-2026.md

@@ -40,6 +40,41 @@ model → classifier as failure floor.
 
 Never compare a hash-embedder run to an ONNX one.
 
+## Re-measured after the prompt fix
+
+The table above is the **baseline at commit `46259b4`**, kept as-is. The prompt fix (query
+tested before fact, plus `repeat_penalty` and a bounded grammar string) was then measured on
+an otherwise idle box — no other eval sharing llama-server, so these latencies are real
+rather than contention.
+
+| | llm-only (0.8B) | cascade+llm (0.8B) | llm-only, thinking off |
+|---|---|---|---|
+| **intent-only accuracy** | 48.7% → **61.8%** | 50.0% → **63.2%** | **67.1%** |
+| full accuracy (intent+slots+gate) | 23.7% → **38.2%** | 32.9% → **47.4%** | **42.1%** |
+| route errors | 2 → **0** | 0 → 0 | **0** |
+| p50 / p95 latency | **1.08s / 1.55s** | **1.04s / 1.53s** | **0.93s / 1.41s** |
+
+Three things this run settles:
+
+1. **The prompt fix holds.** An earlier contended run reported 60.5% / 36.8% for llm-only;
+   the quiet run gives 61.8% / 38.2%. Close enough to call the gain real, and the earlier
+   run's 4-5s latency figures were contention, not the model.
+2. **`query→fact` fell from ×15 to ×7**, and both unparseable replies are gone. Zero route
+   errors in every LLM configuration.
+3. **`note→fact ×4` is real, not noise.** It shows up in the quiet run too. The agent that
+   wrote the prompt fix suspected its own change might have caused it by pulling assertive
+   `запиши что…` phrasings toward fact, and that suspicion stands — all five `ru-note-*`
+   cases now land on fact. Tracked as Vikunja #375.
+
+**Thinking off is the best configuration measured so far**, on both accuracy and latency
+(Vikunja #376). That is worth understanding before flipping: routing is a short
+classification into a fixed enum with grammar-constrained output, so there is little to
+reason about, and the thinking trace mostly gives a small model room to talk itself out of
+the right answer. Phrasing is a different job and needs measuring separately.
+
+Still `6 / 6` missed clarify — the router has no way to say "I don't know" (Vikunja #359).
+That is unchanged by anything here.
+
 ## Findings
 
 ### 1. The resident model does route better — 50.0% vs 36.8%

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",
-      "tokenizer_path": "/opt/maven/models/embedder/tokenizer.json",
+      "model_path": "/opt/maven/models/embedder/multilingual-e5-small/model_quantized.onnx",
+      "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.
-	//   1. The LLM router cannot refuse. LLMRouter.Route hardcodes
-	//      Confidence: 1.0, so the stage-3 clarify gate never fires and an
-	//      unclear utterance becomes a confident wrong action (Vikunja #359).
-	//   2. Extractor.Extract never runs on an LLM decision, so acts arrive with
-	//      no Fn and reminders with no Time.
-	// Turning this on today makes routing more accurate and less safe.
+	// TODO: the default stays false until this lands.
+	// Extractor.Extract never runs on an LLM decision, so acts arrive with no
+	// Fn and reminders with no Time. Turning this on today makes routing more
+	// accurate and less safe.
+	//
+	// The router can now refuse: it answers "unknown" when it cannot route, and
+	// 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")
-	tok := filepath.Join("../../..", "models/embedder/tokenizer.json")
+	model := filepath.Join("../../..", "models/embedder/multilingual-e5-small/model_quantized.onnx")
+	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")
-	tok := filepath.Join("../../..", "models/embedder/tokenizer.json")
+	model := filepath.Join("../../..", "models/embedder/multilingual-e5-small/model_quantized.onnx")
+	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())
@@ -118,3 +175,87 @@ func TestLLMRouterLLMError(t *testing.T) {
 		t.Fatal("want ok=false, err!=nil on llm error")
 	}
 }
+
+// --- slot extraction on top of an LLM decision --------------------------------
+
+// newLLMTestRouter — a router whose route always comes from the mock model.
+func newLLMTestRouter(t *testing.T, out string) *Router {
+	t.Helper()
+	c := NewClassifier(NewHashEmbedder(1024))
+	seedClassifier(t, c)
+	acts := DefaultActMatcher{Fns: []string{"restart", "stop", "run", "backup"}}
+	return New(Config{
+		Classifier: c,
+		Extractor:  Extractor{Time: StubDateTimeParser{}, Acts: acts, Facts: DefaultFactParser{}},
+		Threshold:  0.4,
+		LLM:        NewLLMRouter(mockLLM{out: out}),
+	})
+}
+
+// The model cannot produce a fire time, so without extraction every LLM-routed
+// reminder was dropped as "no time".
+func TestLLMDecisionGetsReminderTime(t *testing.T) {
+	r := newLLMTestRouter(t, `{"intent":"reminder","text":"позвонить маме"}`)
+	d, err := r.Route(context.Background(), "напомни позвонить маме через 2 часа", refNow())
+	if err != nil {
+		t.Fatalf("route: %v", err)
+	}
+	if d.Intent != IntentReminder {
+		t.Fatalf("want reminder, got %v", d.Intent)
+	}
+	if !d.Slots.HasTime || !d.Slots.Time.Equal(refNow().Add(2*time.Hour)) {
+		t.Fatalf("want time now+2h, got %+v", d.Slots)
+	}
+	if d.Slots.Text != "позвонить маме" {
+		t.Fatalf("extraction overwrote the model's text: %q", d.Slots.Text)
+	}
+}
+
+// No time in the utterance ⇒ no time in the slots. Do not invent one; the
+// daemon says it could not read the time.
+func TestLLMReminderWithoutTimeStaysEmpty(t *testing.T) {
+	r := newLLMTestRouter(t, `{"intent":"reminder","text":"позвонить маме"}`)
+	d, err := r.Route(context.Background(), "напомни позвонить маме", refNow())
+	if err != nil {
+		t.Fatalf("route: %v", err)
+	}
+	if d.Slots.HasTime {
+		t.Fatalf("invented a time: %v", d.Slots.Time)
+	}
+}
+
+// An act decision arrived with no Fn, so the tool never ran.
+func TestLLMDecisionGetsActFn(t *testing.T) {
+	r := newLLMTestRouter(t, `{"intent":"act","verb":"restart nginx"}`)
+	d, err := r.Route(context.Background(), "слушай, restart nginx пожалуйста", refNow())
+	if err != nil {
+		t.Fatalf("route: %v", err)
+	}
+	if !d.Slots.HasFn || d.Slots.Fn != "restart" || len(d.Slots.Args) != 1 || d.Slots.Args[0] != "nginx" {
+		t.Fatalf("want fn=restart args=[nginx], got %+v", d.Slots)
+	}
+}
+
+// The model's own slots win; extraction only fills gaps.
+func TestLLMSlotsWinOverExtraction(t *testing.T) {
+	r := newLLMTestRouter(t, `{"intent":"fact","key":"hydration","value":"выпил"}`)
+	d, err := r.Route(context.Background(), "я выпил воду", refNow())
+	if err != nil {
+		t.Fatalf("route: %v", err)
+	}
+	if d.Slots.Key != "hydration" {
+		t.Fatalf("extraction overwrote the model's key: %q", d.Slots.Key)
+	}
+}
+
+// A fact the model left keyless still gets one from the parser.
+func TestLLMFactGetsKeyFromParser(t *testing.T) {
+	r := newLLMTestRouter(t, `{"intent":"fact","text":"я выпил воду"}`)
+	d, err := r.Route(context.Background(), "я выпил воду", refNow())
+	if err != nil {
+		t.Fatalf("route: %v", err)
+	}
+	if !d.Slots.HasKey || d.Slots.Key != "water" {
+		t.Fatalf("want key=water, got %+v", d.Slots)
+	}
+}

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)

internal/router/router.go

@@ -88,6 +88,7 @@ func (r *Router) Route(ctx context.Context, utterance string, now time.Time) (De
 	if r.llm != nil {
 		if d, ok, err := r.llm.Route(ctx, utterance, now); err == nil && ok {
 			d.Utterance = utterance
+			r.fillSlots(ctx, &d, now)
 			return d, nil
 		} else if err != nil {
 			log.Printf("router: llm route fell back to classifier: %v", err)
@@ -118,6 +119,39 @@ func (r *Router) Route(ctx context.Context, utterance string, now time.Time) (De
 	return d, nil
 }
 
+// fillSlots — run stage-2 extraction on an LLM decision and fill only the slots
+// the model left empty. The LLM wins where it answered: it saw the sentence, the
+// parsers are keyword tables. Extraction covers what the model cannot produce at
+// all — a parsed reminder time and an allowlist fn.
+//
+// If a reminder still has no time, leave it missing. The daemon then says it
+// could not read the time; inventing one would set a wrong alarm.
+func (r *Router) fillSlots(ctx context.Context, d *Decision, now time.Time) {
+	ex := r.extractor.Extract(ctx, d.Intent, d.Utterance, now)
+	if !d.Slots.HasTime && ex.HasTime {
+		d.Slots.Time, d.Slots.HasTime = ex.Time, ex.HasTime
+	}
+	if !d.Slots.HasKey && ex.HasKey {
+		d.Slots.Key, d.Slots.Value, d.Slots.HasKey = ex.Key, ex.Value, ex.HasKey
+	}
+	if !d.Slots.HasFn && ex.HasFn {
+		d.Slots.Fn, d.Slots.Args, d.Slots.HasFn = ex.Fn, ex.Args, ex.HasFn
+	}
+	// For an act the model returns the verb in Text ("restart nginx"), which is
+	// often cleaner than the raw utterance ("maven, could you restart nginx").
+	// Try it too when the utterance did not match the allowlist.
+	if d.Intent == IntentAct && !d.Slots.HasFn && r.extractor.Acts != nil &&
+		d.Slots.Text != "" && d.Slots.Text != d.Utterance {
+		if fn, args, ok := r.extractor.Acts.Match(d.Slots.Text); ok {
+			d.Slots.Fn, d.Slots.Args, d.Slots.HasFn = fn, args, true
+		}
+	}
+	if d.Slots.Text == "" {
+		d.Slots.Text = ex.Text
+	}
+	// Stage stays 1: it says who decided the route, and that was the LLM.
+}
+
 // CorrectMisroute — the user corrected a bad classification. Appends a new
 // example for the corrected intent (append-only — grows the classifier, no
 // retrain). Same shape as nudges.outcome tuning cooldowns: more reliable over