Merge commit '74a7088' into overnight-jul31
This commit is contained in:
kami
@@ -0,0 +1,138 @@
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# Phrasing evaluation — 31-07-2026
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How Maven words a nudge, measured instead of argued. Counterpart to
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`ROUTING-EVAL-31-07-2026.md`.
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- Fixture + scorer: `internal/phraser/eval/` (`nudges_v1.json`, 15 cases; `eval.go`, `checks.go`)
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- Reproduce: `MAVEN_LLM_URL=http://127.0.0.1:18099 make eval-phrasing`
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- Model: Qwen3.5-0.8B Q4_K_M, the resident model. Not swapped.
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- Commit: `a40bc55` (prompt fix)
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Every check is a string or length test a human can read and disagree with. No model
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grades another model here.
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## Result
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| | before | after |
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|---|---|---|
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| **cases passing every check** | **0/15** | **13/15** |
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| mood in enum | 6/15 | 15/15 |
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| Russian | 2/15 | 14/15 |
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| length (≤120 chars, ≤16 words) | 13/15 | 15/15 |
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| feminine self-reference | 15/15 | 15/15 |
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| no cringe | 13/15 | 15/15 |
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| on topic | 6/15 | 13/15 |
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| p50 latency | 11.4s | 11.4s |
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Latency did not move and is not good. 11s to word one nudge on this box.
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## The bug reproduced
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Yes, exactly as reported. 7 of 15 messages were the literal string `"..."`, and one was
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`"full voice message"`. Both are text copied straight out of the prompt.
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The system prompt said:
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```
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Respond ONLY with valid JSON: {"response": "full voice message", "mood": "neutral"}
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```
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and the user prompt said:
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```
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Respond as JSON: {"response": "...", "mood": "..."}
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```
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A 0.8B does not read `"..."` as "put your answer here". It reads it as the answer. The
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prompt was a worked example whose worked part was blank, so the model filled the slot by
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copying. This is the whole of finding 1.
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## What else was wrong
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Four separate faults, all prompt-side:
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1. **Placeholder echo** (7 cases) — above.
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2. **Wrong language** (13/15 failed the language check). The prompt was entirely English
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and said "in the user's language (Russian or English)". The model picked English. It is
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never English: the nudge is spoken by a Russian piper voice.
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3. **Rule names are English identifiers.** `netdata_critical`, `service_down`, `break` went
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into the prompt raw. The model cannot nudge about a topic it has not been told in words,
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so 9/15 were off topic. The daemon knows what its own rules mean; now it says so.
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4. **Mood invented** (`"warm"`, twice). The enum was listed in a parenthesis at the end of
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an English sentence. Now it is its own line: "ровно одно из: neutral, happy, thinking,
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tired, confused."
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Plus two non-prompt faults the run exposed:
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- **The no-parse fallback was English.** When the model returned nothing usable, the body
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became `fmt.Sprintf("%s — %s", rule, sev)` — `"water — care"` — and that string went to
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a Russian TTS. Now it falls back to plain Russian.
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- **Durations were English.** `humanDur` returns "3 hours"; it was landing verbatim inside
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Russian sentences. Nudges now use a Russian formatter.
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## Three iterations, and what each taught
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| | score | change |
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|---|---|---|
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| baseline | 0/15 | — |
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| iter 1 | 2/15 | Russian prompt, filled-in examples, Russian durations |
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| iter 2 | 11/15 | required keyword per rule, one example instead of five, Russian fallback |
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| iter 3 | **13/15** | examples moved to topics that are not rules |
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The interesting step is 1 → 2. Fixing the placeholder did not fix the disease, it moved it:
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the model stopped copying `"..."` and started copying my first example instead. Five nudges
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in a row came back as `"Ты не пил воду три часа. Налей стакан."` regardless of the rule.
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**A small model copies the nearest concrete text in its prompt.** That is one failure mode
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with two symptoms. The fix that stuck was making the examples about laundry and a laptop
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battery — topics no rule ever produces, so copying them is visible in the score rather than
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invisibly passing the water cases.
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## Do not oversell 13/15
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Seven of the thirteen passes are the **deterministic fallback**, not the model:
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`"Напоминаю: таблетки."`, `"Сервис не отвечает."`, `"Критический алярм: проверь диск."`,
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`"Ты давно не пил воду."`. Those are strings this commit added to Go. The model returned
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nothing parseable and the fallback scored.
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So the honest reading is roughly **6/15 from the model, 7/15 from a fallback, 2/15 failing**.
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The prompt fix is real — `"..."` is nearly gone and the language and mood checks are clean —
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but a large part of the jump is that failure now degrades into Russian instead of into
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`"water — care"`. That is a genuine improvement for the operator and a weak one for the model.
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The two remaining failures: one `"..."` recurrence (`routine-stretch`) and one meal nudge
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that never says food.
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## Broken, found, not fixed
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1. **`checkFeminine` only catches half the constraint.** It scans for masculine
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self-reference and passed 15/15 both runs — but three messages address the *owner* in
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the feminine: "ты давно не отдыхал**а**", "он не ел". The owner is a man. The check has
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no second-person gender test, so this scores clean while being exactly the persona
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failure the constraint exists to prevent. This is the most important gap in the harness.
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2. **Grammar is not checked at all, and it is bad.** `"Он не ел 11 дней"` (it was 11 hours),
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`"Сонуждились 7 дней"` (not a word), `"Они забыли воду"` (wrong person entirely). Every
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one of these passes all six checks. The fixture measures properties, not fluency, and at
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0.8B fluency is the binding constraint.
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3. **Unit confusion.** The model turns hours into days about a third of the time. The
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prompt now says "11 ч"; it reads it as days.
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4. **11s p50.** Unchanged and untouched here. A nudge the model takes eleven seconds to
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word has missed its moment. Worth its own task.
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5. **The keyword hint is close to teaching to the test.** `ruleKeywords` names the word the
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on-topic check looks for. It is defensible — the daemon genuinely knows its rule topics
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and the model genuinely cannot infer them from `netdata_critical` — but the on-topic
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number is softer than the others because of it.
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## Next steps
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1. **Add a second-person gender check** to `checks.go`. Finding 1 above. Until it exists the
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feminine column means less than it looks like.
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2. **Decide whether the fallback should count as a pass.** Right now `Score` cannot tell a
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model answer from a fallback. Either mark fallback bodies in `PhrasedNudge` or count them
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in their own column. Without that, any future prompt change can score well by failing
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more.
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3. **Attack the 11s.** Nudge phrasing is short and non-interactive; thinking off is the first
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thing to try, as it was for routing (#376).
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4. **Re-measure when #122 lands.** The CPT'd Qwen3-1.7B is the target. 13/15 with seven
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fallbacks is the floor it has to beat, and the fluency problems above are the ones a
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bigger, Russian-trained checkpoint should actually fix.
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@@ -29,7 +29,9 @@ func newLLMReplier(c completer) *llmReplier {
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return &llmReplier{c: c, stub: voice.NewStubReplier()}
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}
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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"}.`
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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).
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Пример: {"response": "Записала, что ты выпил стакан воды.", "mood": "neutral"}
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Никогда не пиши "..." в поле response.`
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func (r *llmReplier) Reply(d router.Decision) string {
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if d.Clarify {
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+136
-13
@@ -184,7 +184,10 @@ func (p *LLMPhraser) PhraseNudge(ctx context.Context, c loop.Candidate) (deliver
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body, _ = parsePhrase(resp)
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}
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if body == "" {
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body = fmt.Sprintf("%s — %s", c.Rule.Name, sevLabel(c.Severity))
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// The model said nothing usable. Say it in Russian anyway — this text
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// goes straight to a Russian piper voice, so the old "water — care"
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// fallback was unspeakable.
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body = fallbackNudge(c)
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}
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if mood == "" {
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mood = "neutral"
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@@ -428,8 +431,33 @@ func (p *LLMPhraser) chatWithSystem(ctx context.Context, system, user string, ma
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return stripThink(content), nil
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}
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// nudgeSystem — the phrasing contract for nudges.
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//
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// Written as filled-in examples, not as a schema with "..." in it. A 0.8B
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// copies whatever sits in the response slot, so a literal placeholder there
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// teaches it to answer with the placeholder. Measured: 7/15 nudges came back
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// as "..." before this. See PHRASING-EVAL-31-07-2026.md.
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//
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// Russian only, feminine self-reference, second person masculine (the owner is
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// a man). One short sentence — the nudge is spoken aloud.
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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").
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Пиши ОДНО короткое напоминание по-русски: не больше 120 символов и не больше 16 слов. Только по делу.
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Запрещено: обращения ("дорогой", "милый"), эмодзи, извинения ("прости", "извини"), вопросы о самочувствии, похвала, больше одного восклицательного знака, английские слова кроме имён сервисов.
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Отвечай ТОЛЬКО одним объектом JSON с полями "response" и "mood".
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"response" — сам текст напоминания.
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"mood" — ровно одно из: neutral, happy, thinking, tired, confused.
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Так выглядит правильный ответ по форме. Темы здесь посторонние — их в запросе не будет:
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{"response": "Стиральная машина закончила. Развесь бельё.", "mood": "neutral"}
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{"response": "Ноутбук на трёх процентах. Я поставила его на зарядку.", "mood": "confused"}
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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.`
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func (p *LLMPhraser) systemPrompt() string {
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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).`
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base := nudgeSystem
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if p.cfg.Persona != "" {
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base = p.cfg.Persona + "\n\n" + base
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}
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@@ -446,22 +474,117 @@ func (p *LLMPhraser) querySystemPrompt() string {
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return base
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}
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// ruleTopics — Russian gloss for each built-in rule name. The rule names are
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// English identifiers; a 0.8B asked to nudge about "netdata_critical" writes
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// about nothing. The daemon knows what its own rules mean, so it says so.
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var ruleTopics = map[string]string{
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"water": "он давно не пил воду",
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"meal": "он давно не ел",
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"break": "он давно без перерыва, пора встать и размяться",
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"service_down": "сервис не отвечает, лежит",
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"netdata_critical": "критический алярм в netdata, проблема с диском или местом",
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}
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// ruleKeywords — the word the message must contain. The 0.8B drifts to
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// whatever topic it saw last unless the required word is named outright.
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var ruleKeywords = map[string]string{
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"water": "воду",
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"meal": "поешь",
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"break": "перерыв",
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"service_down": "сервис",
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"netdata_critical": "диск",
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}
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// ruleTopic turns a rule name into a Russian description of the situation.
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// "routine:зарядка" and "morning:утро" carry their own Russian suffix.
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func ruleTopic(rule string) string {
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if t, ok := ruleTopics[rule]; ok {
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return t
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}
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if i := strings.IndexByte(rule, ':'); i > 0 && i+1 < len(rule) {
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switch rule[:i] {
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case "morning":
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return "утро, пора начать день: " + rule[i+1:]
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default:
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return "пора сделать по распорядку: " + rule[i+1:]
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}
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}
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return rule
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}
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// ruleKeyword — the word the nudge must contain, or "" when the rule name's
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// own Russian suffix already is that word.
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func ruleKeyword(rule string) string {
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if k, ok := ruleKeywords[rule]; ok {
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return k
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}
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if i := strings.IndexByte(rule, ':'); i > 0 && i+1 < len(rule) {
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return rule[i+1:]
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}
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return ""
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}
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// ruDur — duration in Russian. humanDur is English and its output was landing
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// verbatim in the message.
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func ruDur(d time.Duration) string {
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if d < 0 {
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d = 0
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}
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h, m := int(d.Hours()), int(d.Minutes())%60
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switch {
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case h >= 2:
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return fmt.Sprintf("%d ч", h)
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case h == 1 && m >= 30:
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return "полтора часа"
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case h == 1:
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return "час"
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default:
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return fmt.Sprintf("%d мин", m)
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}
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}
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// fallbackNudge — plain Russian for when the model returns nothing parseable.
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var fallbackNudges = map[string]string{
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"water": "Ты давно не пил воду.",
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"meal": "Ты давно не ел, поешь.",
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"break": "Пора сделать перерыв.",
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"service_down": "Сервис не отвечает.",
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"netdata_critical": "Критический алярм: проверь диск.",
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}
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func fallbackNudge(c loop.Candidate) string {
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if s, ok := fallbackNudges[c.Rule.Name]; ok {
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return s
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}
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if kw := ruleKeyword(c.Rule.Name); kw != "" {
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return "Напоминаю: " + kw + "."
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}
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return "Напоминаю о деле."
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}
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func buildNudgePrompt(c loop.Candidate) string {
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var ctxParts []string
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ctxParts = append(ctxParts, fmt.Sprintf("Rule: %s", c.Rule.Name))
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ctxParts = append(ctxParts, fmt.Sprintf("Severity: %s", sevLabel(c.Severity)))
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ctxParts = append(ctxParts, "Ситуация: "+ruleTopic(c.Rule.Name))
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if f, ok := c.State.Facts[c.Rule.Name]; ok && f.Key != "" && f.Key != c.Rule.Name {
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ctxParts = append(ctxParts, "Что именно: "+f.Key)
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}
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if d, ok := c.State.Since(c.Rule.Name); ok {
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ctxParts = append(ctxParts, fmt.Sprintf("Duration since last event: %s", humanDur(d)))
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ctxParts = append(ctxParts, "Прошло: "+ruDur(d))
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}
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switch sevLabel(c.Severity) {
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case "alarm":
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ctxParts = append(ctxParts, "Срочно, скажи прямо.")
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case "ops":
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ctxParts = append(ctxParts, "Это про сервер, не про здоровье.")
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}
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tail := "Напиши напоминание про эту ситуацию. Одно предложение, по-русски, в JSON."
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if kw := ruleKeyword(c.Rule.Name); kw != "" {
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// Last line on purpose: a 0.8B weights the end of the prompt hardest,
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// and without the required word it drifts back to the examples.
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tail += " Ответ ДОЛЖЕН содержать слово «" + kw + "»."
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}
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return fmt.Sprintf(
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`Generate a nudge message. Context:
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%s
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Respond as JSON: {"response": "...", "mood": "..."}`,
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strings.Join(ctxParts, "\n"),
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)
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return strings.Join(ctxParts, "\n") + "\n\n" + tail
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}
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type responseMood struct {
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Reference in New Issue
Block a user