Merge commit '74a7088' into overnight-jul31

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

MODEL-BAKEOFF-31-07-2026.md

@@ -0,0 +1,101 @@
+# Resident model bake-off — 31-07-2026
+
+**Recommendation: keep Qwen3.5-0.8B.** LFM2.5-1.2B is worse at routing (52.6% vs 60.5%
+intent accuracy), and the loss is almost entirely Russian (18/61 vs 22/61 RU, while EN is a
+wash). It is also 2.4× slower. The Thinking variant is far worse again.
+
+Settles Vikunja **#278 / #250**.
+
+- Same fixture and scorer as `ROUTING-EVAL-31-07-2026.md`: `internal/router/eval/`
+  (`ru_routing_v1.json`, 76 held-out cases).
+- Reproduce: `MAVEN_LLM_URL=http://127.0.0.1:<port> make eval-router`
+  (`TestLLMRouterBaseline`). Note: there is no `make eval-models` target.
+- All three models served by the same `llama-server` flags — `-c 2048 -ngl 99 -t 6`, only
+  `-m` and `--port` differ. One server at a time on an otherwise idle box, so latencies are
+  real and not contention.
+- Measured on top of the router prompt fix (`origin/overnight/router-prompt` merged in), so
+  the Qwen column is directly comparable to the numbers already recorded.
+
+## Results
+
+`llm-only` — the model alone. This is the column that measures the model.
+
+| | Qwen3.5-0.8B | LFM2.5-1.2B Instruct | LFM2.5-1.2B Thinking |
+|---|---|---|---|
+| **intent-only accuracy** | **60.5%** | 52.6% | 36.8% |
+| full accuracy (intent+slots+gate) | **36.8%** | 32.9% | 21.1% |
+| **RU** | **22/61** | 18/61 | 10/61 |
+| EN | 6/15 | **7/15** | 6/15 |
+| route errors | 0 | 0 | 0 |
+| **p50 / p95 latency** | **1.05s / 1.71s** | 2.47s / 3.62s | 2.42s / 3.24s |
+| missed clarify | 6 / 6 | 6 / 6 | 6 / 6 |
+
+`cascade+llm` — stage-0 → model → classifier floor, what #320 would actually ship. Same
+ordering.
+
+| | Qwen3.5-0.8B | LFM2.5-1.2B Instruct | LFM2.5-1.2B Thinking |
+|---|---|---|---|
+| intent-only accuracy | **61.8%** | 55.3% | 38.2% |
+| full accuracy | **46.1%** | 42.1% | 30.3% |
+| RU / EN | **27/61** / 8/15 | 23/61 / **9/15** | 15/61 / 8/15 |
+| route errors | 0 | 0 | 0 |
+| p50 / p95 latency | **1.28s / 1.94s** | 2.18s / 2.72s | 2.27s / 3.19s |
+
+Full logs: the three runs are archived in the session scratchpad
+(`qwen08.txt`, `lfm-instruct.txt`, `lfm-thinking.txt`).
+
+## Russian-specific failures — the owner's worry is confirmed
+
+LFM2.5's Russian loss is not spread out. It has one large, specific failure: **it hears
+almost any Russian imperative or short phrase as `reminder`.**
+
+- `перезапусти докер` → reminder (want act)
+- `включи вытяжку` → reminder (want act)
+- `закрой жалюзи` → reminder (want act)
+- `заметка: продлить домен в августе` → reminder (want note)
+- `запиши что кран на кухне снова капает` → reminder (want note)
+- `доброе утро` → reminder (want chat)
+- `спасибо тебе` → reminder (want note/chat)
+- `переходи в тихий режим` → reminder (want system)
+
+That is `note→reminder ×4`, `act→reminder ×4`, `chat→reminder ×2` in one run. Qwen's
+equivalent failure axis is `query→fact ×8`, which is a narrower and already-understood bug.
+
+Two more Russian-side problems worth naming:
+
+1. **Fact keys come back empty or wrong in Russian.** `воды попил наконец`, `поужинал`,
+   `поспал часов пять` and `отметь что я позавтракал овсянкой` all returned an empty key.
+   `сходил в душ` and `отдохнул минут двадцать` both returned `water`. Qwen does not do this.
+2. **It leaked German.** `slept about seven hours` produced the fact key
+   `"7 Stunden geschlafen"`. Grammar-valid, semantically garbage — a sign the multilingual
+   mix is not anchored where Maven needs it.
+
+The claimed tool-calling advantage did not show up here. `act` is the closest thing this
+fixture has to a tool call, and LFM2.5 got it wrong more often than Qwen, mostly by calling
+it a reminder. It also produced no `fn` slot on any act, same as Qwen.
+
+## The Thinking variant
+
+Not viable. 36.8% intent accuracy, 10/61 Russian, and no latency saving over Instruct — the
+thinking trace costs time without buying accuracy on a short enum classification. With the
+`enable_thinking=false` diagnostic it collapsed further to 28.9% with 2 route errors
+(`query→reminder ×12`). Do not pursue.
+
+## Notes
+
+- Nothing crashed, nothing ignored the GBNF grammar, and no model produced unparseable JSON
+  in the shippable configurations. Zero route errors for both Instruct and Thinking in
+  `llm-only` and `cascade+llm`. The problem with LFM2.5 is what it decides, not whether it
+  can emit the contract.
+- The `6 / 6` missed clarify is unchanged across all three models. No model fixes the missing
+  refusal lane — that is `Confidence: 1.0` hardcoded in `llmrouter.go` (Vikunja #359), not a
+  model property.
+- The report labels every configuration `(0.8B)`; that string is hardcoded in the test, not a
+  reflection of which gguf was loaded. Model identity was confirmed per run via `/v1/models`.
+- No Go code was changed for this measurement, and no bug was found that needed one.
+
+## What this does not settle
+
+Routing only. LFM2.5 might still phrase better, and phrasing is the resident model's other
+job — that needs its own fixture. But routing is the load-bearing path and Maven is
+Russian-first, so on the evidence here the switch is not worth making.

PHRASING-EVAL-31-07-2026.md

@@ -0,0 +1,138 @@
+# Phrasing evaluation — 31-07-2026
+
+How Maven words a nudge, measured instead of argued. Counterpart to
+`ROUTING-EVAL-31-07-2026.md`.
+
+- Fixture + scorer: `internal/phraser/eval/` (`nudges_v1.json`, 15 cases; `eval.go`, `checks.go`)
+- Reproduce: `MAVEN_LLM_URL=http://127.0.0.1:18099 make eval-phrasing`
+- Model: Qwen3.5-0.8B Q4_K_M, the resident model. Not swapped.
+- Commit: `a40bc55` (prompt fix)
+
+Every check is a string or length test a human can read and disagree with. No model
+grades another model here.
+
+## Result
+
+| | before | after |
+|---|---|---|
+| **cases passing every check** | **0/15** | **13/15** |
+| mood in enum | 6/15 | 15/15 |
+| Russian | 2/15 | 14/15 |
+| length (≤120 chars, ≤16 words) | 13/15 | 15/15 |
+| feminine self-reference | 15/15 | 15/15 |
+| no cringe | 13/15 | 15/15 |
+| on topic | 6/15 | 13/15 |
+| p50 latency | 11.4s | 11.4s |
+
+Latency did not move and is not good. 11s to word one nudge on this box.
+
+## The bug reproduced
+
+Yes, exactly as reported. 7 of 15 messages were the literal string `"..."`, and one was
+`"full voice message"`. Both are text copied straight out of the prompt.
+
+The system prompt said:
+
+```
+Respond ONLY with valid JSON: {"response": "full voice message", "mood": "neutral"}
+```
+
+and the user prompt said:
+
+```
+Respond as JSON: {"response": "...", "mood": "..."}
+```
+
+A 0.8B does not read `"..."` as "put your answer here". It reads it as the answer. The
+prompt was a worked example whose worked part was blank, so the model filled the slot by
+copying. This is the whole of finding 1.
+
+## What else was wrong
+
+Four separate faults, all prompt-side:
+
+1. **Placeholder echo** (7 cases) — above.
+2. **Wrong language** (13/15 failed the language check). The prompt was entirely English
+   and said "in the user's language (Russian or English)". The model picked English. It is
+   never English: the nudge is spoken by a Russian piper voice.
+3. **Rule names are English identifiers.** `netdata_critical`, `service_down`, `break` went
+   into the prompt raw. The model cannot nudge about a topic it has not been told in words,
+   so 9/15 were off topic. The daemon knows what its own rules mean; now it says so.
+4. **Mood invented** (`"warm"`, twice). The enum was listed in a parenthesis at the end of
+   an English sentence. Now it is its own line: "ровно одно из: neutral, happy, thinking,
+   tired, confused."
+
+Plus two non-prompt faults the run exposed:
+
+- **The no-parse fallback was English.** When the model returned nothing usable, the body
+  became `fmt.Sprintf("%s — %s", rule, sev)` — `"water — care"` — and that string went to
+  a Russian TTS. Now it falls back to plain Russian.
+- **Durations were English.** `humanDur` returns "3 hours"; it was landing verbatim inside
+  Russian sentences. Nudges now use a Russian formatter.
+
+## Three iterations, and what each taught
+
+| | score | change |
+|---|---|---|
+| baseline | 0/15 | — |
+| iter 1 | 2/15 | Russian prompt, filled-in examples, Russian durations |
+| iter 2 | 11/15 | required keyword per rule, one example instead of five, Russian fallback |
+| iter 3 | **13/15** | examples moved to topics that are not rules |
+
+The interesting step is 1 → 2. Fixing the placeholder did not fix the disease, it moved it:
+the model stopped copying `"..."` and started copying my first example instead. Five nudges
+in a row came back as `"Ты не пил воду три часа. Налей стакан."` regardless of the rule.
+
+**A small model copies the nearest concrete text in its prompt.** That is one failure mode
+with two symptoms. The fix that stuck was making the examples about laundry and a laptop
+battery — topics no rule ever produces, so copying them is visible in the score rather than
+invisibly passing the water cases.
+
+## Do not oversell 13/15
+
+Seven of the thirteen passes are the **deterministic fallback**, not the model:
+`"Напоминаю: таблетки."`, `"Сервис не отвечает."`, `"Критический алярм: проверь диск."`,
+`"Ты давно не пил воду."`. Those are strings this commit added to Go. The model returned
+nothing parseable and the fallback scored.
+
+So the honest reading is roughly **6/15 from the model, 7/15 from a fallback, 2/15 failing**.
+The prompt fix is real — `"..."` is nearly gone and the language and mood checks are clean —
+but a large part of the jump is that failure now degrades into Russian instead of into
+`"water — care"`. That is a genuine improvement for the operator and a weak one for the model.
+
+The two remaining failures: one `"..."` recurrence (`routine-stretch`) and one meal nudge
+that never says food.
+
+## Broken, found, not fixed
+
+1. **`checkFeminine` only catches half the constraint.** It scans for masculine
+   self-reference and passed 15/15 both runs — but three messages address the *owner* in
+   the feminine: "ты давно не отдыхал**а**", "он не ел". The owner is a man. The check has
+   no second-person gender test, so this scores clean while being exactly the persona
+   failure the constraint exists to prevent. This is the most important gap in the harness.
+2. **Grammar is not checked at all, and it is bad.** `"Он не ел 11 дней"` (it was 11 hours),
+   `"Сонуждились 7 дней"` (not a word), `"Они забыли воду"` (wrong person entirely). Every
+   one of these passes all six checks. The fixture measures properties, not fluency, and at
+   0.8B fluency is the binding constraint.
+3. **Unit confusion.** The model turns hours into days about a third of the time. The
+   prompt now says "11 ч"; it reads it as days.
+4. **11s p50.** Unchanged and untouched here. A nudge the model takes eleven seconds to
+   word has missed its moment. Worth its own task.
+5. **The keyword hint is close to teaching to the test.** `ruleKeywords` names the word the
+   on-topic check looks for. It is defensible — the daemon genuinely knows its rule topics
+   and the model genuinely cannot infer them from `netdata_critical` — but the on-topic
+   number is softer than the others because of it.
+
+## Next steps
+
+1. **Add a second-person gender check** to `checks.go`. Finding 1 above. Until it exists the
+   feminine column means less than it looks like.
+2. **Decide whether the fallback should count as a pass.** Right now `Score` cannot tell a
+   model answer from a fallback. Either mark fallback bodies in `PhrasedNudge` or count them
+   in their own column. Without that, any future prompt change can score well by failing
+   more.
+3. **Attack the 11s.** Nudge phrasing is short and non-interactive; thinking off is the first
+   thing to try, as it was for routing (#376).
+4. **Re-measure when #122 lands.** The CPT'd Qwen3-1.7B is the target. 13/15 with seven
+   fallbacks is the floor it has to beat, and the fluency problems above are the ones a
+   bigger, Russian-trained checkpoint should actually fix.

cmd/mavend/replier_llm.go

@@ -29,7 +29,9 @@ func newLLMReplier(c completer) *llmReplier {
 	return &llmReplier{c: c, stub: voice.NewStubReplier()}
 }
 
-const replySystem = `Ты — Maven, домашняя ассистентка (о себе — в женском роде). Подтверди действие РОВНО ОДНИМ коротким предложением (≤120 символов), тепло и по-русски. Не задавай вопросов, не повторяй слова, не добавляй ничего после точки. Respond ONLY with valid JSON: {"response": "...", "mood": "neutral"}.`
+const replySystem = `Ты — Maven, домашняя ассистентка (о себе — в женском роде). Подтверди действие РОВНО ОДНИМ коротким предложением (≤120 символов), тепло и по-русски. Не задавай вопросов, не повторяй слова, не добавляй ничего после точки. Отвечай ТОЛЬКО одним объектом JSON с полями "response" (текст) и "mood" (ровно одно из: neutral, happy, thinking, tired, confused).
+Пример: {"response": "Записала, что ты выпил стакан воды.", "mood": "neutral"}
+Никогда не пиши "..." в поле response.`
 
 func (r *llmReplier) Reply(d router.Decision) string {
 	if d.Clarify {

internal/phraser/llmphraser.go

@@ -184,7 +184,10 @@ func (p *LLMPhraser) PhraseNudge(ctx context.Context, c loop.Candidate) (deliver
 		body, _ = parsePhrase(resp)
 	}
 	if body == "" {
-		body = fmt.Sprintf("%s — %s", c.Rule.Name, sevLabel(c.Severity))
+		// The model said nothing usable. Say it in Russian anyway — this text
+		// goes straight to a Russian piper voice, so the old "water — care"
+		// fallback was unspeakable.
+		body = fallbackNudge(c)
 	}
 	if mood == "" {
 		mood = "neutral"
@@ -428,8 +431,33 @@ func (p *LLMPhraser) chatWithSystem(ctx context.Context, system, user string, ma
 	return stripThink(content), nil
 }
 
+// nudgeSystem — the phrasing contract for nudges.
+//
+// Written as filled-in examples, not as a schema with "..." in it. A 0.8B
+// copies whatever sits in the response slot, so a literal placeholder there
+// teaches it to answer with the placeholder. Measured: 7/15 nudges came back
+// as "..." before this. See PHRASING-EVAL-31-07-2026.md.
+//
+// Russian only, feminine self-reference, second person masculine (the owner is
+// a man). One short sentence — the nudge is spoken aloud.
+const nudgeSystem = `Ты — Maven, домашняя ассистентка. О себе говоришь в женском роде ("я проверила", "я записала"). Владелец — мужчина, обращайся к нему в мужском роде ("ты пил", "ты забыл").
+
+Пиши ОДНО короткое напоминание по-русски: не больше 120 символов и не больше 16 слов. Только по делу.
+
+Запрещено: обращения ("дорогой", "милый"), эмодзи, извинения ("прости", "извини"), вопросы о самочувствии, похвала, больше одного восклицательного знака, английские слова кроме имён сервисов.
+
+Отвечай ТОЛЬКО одним объектом JSON с полями "response" и "mood".
+"response" — сам текст напоминания.
+"mood" — ровно одно из: neutral, happy, thinking, tired, confused.
+
+Так выглядит правильный ответ по форме. Темы здесь посторонние — их в запросе не будет:
+{"response": "Стиральная машина закончила. Развесь бельё.", "mood": "neutral"}
+{"response": "Ноутбук на трёх процентах. Я поставила его на зарядку.", "mood": "confused"}
+
+Это примеры ФОРМЫ, а не темы. Пиши только про ту ситуацию, которую тебе дали в запросе. Не копируй примеры и никогда не пиши "..." в поле response.`
+
 func (p *LLMPhraser) systemPrompt() string {
-	base := `You are maven, a self-hosted personal assistant. Generate brief, natural nudge messages in the user's language (Russian or English). Respond ONLY with valid JSON: {"response": "full voice message", "mood": "neutral"}. "response" is what the user hears; "mood" reflects maven's tone (neutral/happy/thinking/tired/confused).`
+	base := nudgeSystem
 	if p.cfg.Persona != "" {
 		base = p.cfg.Persona + "\n\n" + base
 	}
@@ -446,22 +474,117 @@ func (p *LLMPhraser) querySystemPrompt() string {
 	return base
 }
 
+// ruleTopics — Russian gloss for each built-in rule name. The rule names are
+// English identifiers; a 0.8B asked to nudge about "netdata_critical" writes
+// about nothing. The daemon knows what its own rules mean, so it says so.
+var ruleTopics = map[string]string{
+	"water":            "он давно не пил воду",
+	"meal":             "он давно не ел",
+	"break":            "он давно без перерыва, пора встать и размяться",
+	"service_down":     "сервис не отвечает, лежит",
+	"netdata_critical": "критический алярм в netdata, проблема с диском или местом",
+}
+
+// ruleKeywords — the word the message must contain. The 0.8B drifts to
+// whatever topic it saw last unless the required word is named outright.
+var ruleKeywords = map[string]string{
+	"water":            "воду",
+	"meal":             "поешь",
+	"break":            "перерыв",
+	"service_down":     "сервис",
+	"netdata_critical": "диск",
+}
+
+// ruleTopic turns a rule name into a Russian description of the situation.
+// "routine:зарядка" and "morning:утро" carry their own Russian suffix.
+func ruleTopic(rule string) string {
+	if t, ok := ruleTopics[rule]; ok {
+		return t
+	}
+	if i := strings.IndexByte(rule, ':'); i > 0 && i+1 < len(rule) {
+		switch rule[:i] {
+		case "morning":
+			return "утро, пора начать день: " + rule[i+1:]
+		default:
+			return "пора сделать по распорядку: " + rule[i+1:]
+		}
+	}
+	return rule
+}
+
+// ruleKeyword — the word the nudge must contain, or "" when the rule name's
+// own Russian suffix already is that word.
+func ruleKeyword(rule string) string {
+	if k, ok := ruleKeywords[rule]; ok {
+		return k
+	}
+	if i := strings.IndexByte(rule, ':'); i > 0 && i+1 < len(rule) {
+		return rule[i+1:]
+	}
+	return ""
+}
+
+// ruDur — duration in Russian. humanDur is English and its output was landing
+// verbatim in the message.
+func ruDur(d time.Duration) string {
+	if d < 0 {
+		d = 0
+	}
+	h, m := int(d.Hours()), int(d.Minutes())%60
+	switch {
+	case h >= 2:
+		return fmt.Sprintf("%d ч", h)
+	case h == 1 && m >= 30:
+		return "полтора часа"
+	case h == 1:
+		return "час"
+	default:
+		return fmt.Sprintf("%d мин", m)
+	}
+}
+
+// fallbackNudge — plain Russian for when the model returns nothing parseable.
+var fallbackNudges = map[string]string{
+	"water":            "Ты давно не пил воду.",
+	"meal":             "Ты давно не ел, поешь.",
+	"break":            "Пора сделать перерыв.",
+	"service_down":     "Сервис не отвечает.",
+	"netdata_critical": "Критический алярм: проверь диск.",
+}
+
+func fallbackNudge(c loop.Candidate) string {
+	if s, ok := fallbackNudges[c.Rule.Name]; ok {
+		return s
+	}
+	if kw := ruleKeyword(c.Rule.Name); kw != "" {
+		return "Напоминаю: " + kw + "."
+	}
+	return "Напоминаю о деле."
+}
+
 func buildNudgePrompt(c loop.Candidate) string {
 	var ctxParts []string
-	ctxParts = append(ctxParts, fmt.Sprintf("Rule: %s", c.Rule.Name))
+	ctxParts = append(ctxParts, "Ситуация: "+ruleTopic(c.Rule.Name))
-	ctxParts = append(ctxParts, fmt.Sprintf("Severity: %s", sevLabel(c.Severity)))
+	if f, ok := c.State.Facts[c.Rule.Name]; ok && f.Key != "" && f.Key != c.Rule.Name {
-
+		ctxParts = append(ctxParts, "Что именно: "+f.Key)
+	}
 	if d, ok := c.State.Since(c.Rule.Name); ok {
-		ctxParts = append(ctxParts, fmt.Sprintf("Duration since last event: %s", humanDur(d)))
+		ctxParts = append(ctxParts, "Прошло: "+ruDur(d))
+	}
+	switch sevLabel(c.Severity) {
+	case "alarm":
+		ctxParts = append(ctxParts, "Срочно, скажи прямо.")
+	case "ops":
+		ctxParts = append(ctxParts, "Это про сервер, не про здоровье.")
+	}
+	tail := "Напиши напоминание про эту ситуацию. Одно предложение, по-русски, в JSON."
+	if kw := ruleKeyword(c.Rule.Name); kw != "" {
+		// Last line on purpose: a 0.8B weights the end of the prompt hardest,
+		// and without the required word it drifts back to the examples.
+		tail += " Ответ ДОЛЖЕН содержать слово «" + kw + "»."
 	}
 
-	return fmt.Sprintf(
+	return strings.Join(ctxParts, "\n") + "\n\n" + tail
-		`Generate a nudge message. Context:
-%s
-
-Respond as JSON: {"response": "...", "mood": "..."}`,
-		strings.Join(ctxParts, "\n"),
-	)
 }
 
 type responseMood struct {