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Author SHA1 Message Date
kami 5d5b0cfd49 Merge branch 'worktree-agent-a0ab2a9b7439296e3' into overnight-jul31 2026-07-31 11:37:53 +04:00
kami bd16ca69e5 Let the LLM router answer "unknown" when it cannot route
Chose an 8th enum value over a confidence number: the model already picks
one enum token, so it costs nothing in the grammar, while a score from a
0.8B model would be uncalibrated noise. A refusal returns "no decision"
with no error, which is the fall-through the caller already uses for a
bad parse, so the classifier and its clarify gate take the turn.

Reviewers: the prompt's counter-examples matter most — a small model will
over-use any easy escape hatch. The training workspace copy of the prompt
still needs the same edit (Vikunja #362).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
2026-07-31 11:35:37 +04:00
kami 0ed386eca6 Re-measure the router on a quiet box and record the numbers
The earlier before/after was taken while another eval shared
llama-server. This run had the box to itself.

Intent accuracy 61.8% llm-only, 63.2% cascade, 67.1% with thinking off.
The prompt fix holds. note→fact shows up here too, so it is real.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
2026-07-31 11:34:57 +04:00
4 changed files with 133 additions and 11 deletions
+35
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@@ -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%
+7 -7
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@@ -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
+33 -3
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@@ -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:
+58 -1
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@@ -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())