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.

RECALL-EVAL-31-07-2026.md

@@ -143,14 +143,97 @@ the vector memory table was written by the old model, so after this deploy they
 against a new query. A live database needs every note and fact re-embedded before recall works at
 all. Filed as its own task.
 
+## Margin gate — 31-07-2026, third run
+
+Next-steps item 3, done. The absolute gate is replaced by a **margin gate**: answer only when the
+top hit beats the runner-up by more than delta (`top1 − top2 > δ`). Same fixture, same e5 embedder,
+same store as the run above. `internal/memory/gate.go` holds the check; both read paths call it
+(`cmd/mavend/recall.go` and the notes-RAG branch in `voice.go`). New knob `voice.query_min_margin`
+in `deploy/mavend.json`, default 0.008.
+
+### Why the absolute gate could not work, in one line of data
+
+The harness now prints the margin distributions, and they barely overlap where the raw scores
+overlap completely:
+
+| | top-1 score | margin (top1 − top2) |
+|---|---|---|
+| right note first (n=18) | min 0.810, median 0.862, max 0.890 | min 0.001, median 0.029, max 0.053 |
+| must stay silent (n=5) | min 0.795, median 0.815, max 0.835 | min 0.000, median 0.002, **max 0.019** |
+
+Four of the five must-be-silent cases have a margin at or under 0.002 — when there is nothing to
+recall, e5 finds several notes equally close and no clear winner. That is the signal the absolute
+score throws away.
+
+### The delta sweep
+
+Absolute gate held at 0.55 throughout.
+
+```
+delta 0.000: answered 18/25 (72%)  false recall 5/5
+delta 0.002: answered 17/25 (68%)  false recall 3/5
+delta 0.005: answered 17/25 (68%)  false recall 2/5
+delta 0.008: answered 17/25 (68%)  false recall 1/5   <- chosen
+delta 0.010: answered 15/25 (60%)  false recall 1/5
+delta 0.012: answered 14/25 (56%)  false recall 1/5
+delta 0.015: answered 12/25 (48%)  false recall 1/5
+delta 0.020: answered 11/25 (44%)  false recall 0/5
+delta 0.025: answered  9/25 (36%)  false recall 0/5
+delta 0.030: answered  8/25 (32%)  false recall 0/5
+delta 0.040: answered  4/25 (16%)  false recall 0/5
+delta 0.050: answered  2/25 ( 8%)  false recall 0/5
+delta 0.060: answered  0/25 ( 0%)  false recall 0/5
+```
+
+### Chosen: δ = 0.008
+
+It is the best point on the frontier, not a taste call. **0.008 dominates 0.010, 0.012 and 0.015
+outright** — same 1/5 false recall, 8 to 20 points more real recall. Everything below it buys recall
+back only by admitting more false recalls (0.005 → 2/5, 0.002 → 3/5). The next real improvement is
+0.020 at 0/5 false, and it costs 24 points of recall to get there.
+
+The brief's bar was "recall above 60% with false recall at 1/5 or better". 0.008 clears it with room:
+68% and 1/5.
+
+### Before / after
+
+| | absolute gate 0.55 (previous) | margin gate δ=0.008 |
+|---|---|---|
+| recall@1 (ranking, ungated) | 72.0% (18/25) | 72.0% (18/25) — unchanged, the gate does not rank |
+| **answered after the gate** | 72.0% (18/25) | **68.0% (17/25)** |
+| **false recall** | **5/5 (100%)** | **1/5 (20%)** |
+| fixture cases passed | 18/30 | **21/30** |
+
+Four false recalls removed for one real answer. That is the trade the spec asks for — she is not a
+guesser-of-truth. The one survivor is `en-pref-025` ("should i be offered wine"), which recalls a
+filler note at 0.796 with a 0.019 margin: the widest silent-case margin in the fixture, and it sits
+inside the real-recall range, so no delta removes it without taking real answers with it.
+
+### Does the absolute cutoff still earn its keep? Marginally — kept
+
+On this fixture with e5 it is a **no-op**: the lowest right-note score is 0.791, so 0.55 rejects
+nothing the margin does not already reject. It is kept for two reasons, neither glamorous. It still
+does real work for the hash embedder (its own sweep shows answers dropping from 16% to 0% between
+0.30 and 0.50), and it is the only thing standing between the user and a reply built from a store
+where everything is far away but one row happens to be a little less far — a near-empty database, or
+the stale-vector case below. Cheap insurance, no measured cost. If a later embedder makes it bite,
+the sweep is one command.
+
+### Caveat on the numbers
+
+Five must-be-silent cases is a thin basis for a 4-point decision. 1/5 and 2/5 differ by one case.
+The shape of the frontier is trustworthy — margins separate, absolute scores do not — but δ=0.008
+itself should be re-read off a bigger fixture (next-steps item 6) before anyone defends the third
+decimal.
+
 ## 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
    change plus a prefix in `onnxembedder.go`, re-measurable in one command.
 2. **Re-run `make eval-recall`, then set the gate from the sweep** — not before. Any
    `query_min_score` picked against today's embedder describes a model on its way out.
-3. **Replace the absolute-score gate with a margin gate** (`top1 − top2 > δ`) — as the routing eval
-   concluded, absolute cosine cannot see a flat distribution.
+3. ~~**Replace the absolute-score gate with a margin gate**~~ — done, see the section above.
+   δ=0.008, false recall 5/5 → 1/5.
 4. **Delete or repair the dead `memStore` branch** at `voice.go:776` — search before the gate,
    gate it separately, or restrict it to facts and say so.
 5. **Add a mild time decay to ranking** — the newest statement of a preference is the true one.

cmd/mavend/recall.go

@@ -8,16 +8,13 @@ import "github.com/kami/maven/internal/memory"
 // that can answer "when did I last …?" from a captured fact). A note hit here
 // is redundant with the notes-RAG path — by design; the two indexes can diverge
 // once the backend is swapped for a persistent/external store. ok=false when
-// there's no hit above the threshold or the hit carries no text.
-func bestRecall(results []memory.Result, min float64) (string, bool) {
-	if len(results) == 0 {
+// the hit fails the confidence gate (see memory.Confident: an absolute floor
+// plus a margin over the runner-up) or carries no text.
+func bestRecall(results []memory.Result, minScore, minMargin float64) (string, bool) {
+	if !memory.Confident(results, minScore, minMargin) {
 		return "", false
 	}
-	top := results[0]
-	if top.Score < min {
-		return "", false
-	}
-	text := top.Meta["text"]
+	text := results[0].Meta["text"]
 	if text == "" {
 		return "", false
 	}

cmd/mavend/recall_test.go

@@ -8,23 +8,24 @@ import (
 
 func TestBestRecall(t *testing.T) {
 	const min = 0.55
+	const margin = 0.008
 
 	t.Run("empty results", func(t *testing.T) {
-		if _, ok := bestRecall(nil, min); ok {
+		if _, ok := bestRecall(nil, min, margin); ok {
 			t.Error("empty results returned ok")
 		}
 	})
 
 	t.Run("top below threshold", func(t *testing.T) {
 		res := []memory.Result{{Score: 0.4, Meta: map[string]string{"text": "выпил воды"}}}
-		if _, ok := bestRecall(res, min); ok {
+		if _, ok := bestRecall(res, min, margin); ok {
 			t.Error("below-threshold hit returned ok")
 		}
 	})
 
 	t.Run("hit without text meta", func(t *testing.T) {
 		res := []memory.Result{{Score: 0.9, Meta: map[string]string{"type": "fact"}}}
-		if _, ok := bestRecall(res, min); ok {
+		if _, ok := bestRecall(res, min, margin); ok {
 			t.Error("textless hit returned ok")
 		}
 	})
@@ -34,7 +35,7 @@ func TestBestRecall(t *testing.T) {
 			{Score: 0.82, Meta: map[string]string{"text": "выпил воды в три часа", "type": "fact"}},
 			{Score: 0.60, Meta: map[string]string{"text": "другое"}},
 		}
-		got, ok := bestRecall(res, min)
+		got, ok := bestRecall(res, min, margin)
 		if !ok {
 			t.Fatal("clearing hit not returned")
 		}
@@ -42,4 +43,16 @@ func TestBestRecall(t *testing.T) {
 			t.Errorf("wrong text: %q", got)
 		}
 	})
+
+	// The runner-up is almost as close, so the embedder cannot tell the two
+	// notes apart. Silence beats reading back a coin flip.
+	t.Run("runner-up too close", func(t *testing.T) {
+		res := []memory.Result{
+			{Score: 0.860, Meta: map[string]string{"text": "выпил воды в три часа"}},
+			{Score: 0.858, Meta: map[string]string{"text": "другое"}},
+		}
+		if _, ok := bestRecall(res, min, margin); ok {
+			t.Error("thin-margin hit returned ok")
+		}
+	})
 }

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 {

cmd/mavend/voice.go

@@ -206,11 +206,11 @@ func wireVoice(cfg *config.Config, coreAPI ipc.CoreAPI, phr phraser.Phraser, mem
 	if threshold <= 0 {
 		threshold = config.DefaultRouterThreshold
 	}
-	// Both routing paths are weak on held-out utterances — the classifier gets
-	// 36.8% of intents right, the resident model 50.0% and much slower. Off by
-	// default (see config.VoiceConfig.LLMRouter); the classifier always stays
-	// wired as the fallback, so a model error never breaks a turn.
-	rtr := buildRouter(emb, matcher, threshold, pickLLMRouter(cfg.Voice.LLMRouter, llmClient))
+	// The resident model routes by default: 63.2% of held-out intents right
+	// against the classifier's 50.0%, at about 1s a turn instead of 30ms (see
+	// config.VoiceConfig.LLMRouter). The classifier always stays wired as the
+	// fallback, so a model error never breaks a turn.
+	rtr := buildRouter(emb, matcher, threshold, pickLLMRouter(cfg.Voice.UseLLMRouter(), llmClient))
 
 	// ----- sessions registry (shared with voicesink) -----
 	sessions := voice.NewSessions()
@@ -257,6 +257,7 @@ func wireVoice(cfg *config.Config, coreAPI ipc.CoreAPI, phr phraser.Phraser, mem
 		clarifyStore:     clarifyStore,
 		extractor:        router.Extractor{Time: timeParser, Acts: matcher, Facts: router.DefaultFactParser{}},
 		queryMinScore:    cfg.Voice.QueryMinScore,
+		queryMinMargin:   cfg.Voice.QueryMinMargin,
 		timeParser:       timeParser,
 		ecosystem:        eco,
 	}
@@ -300,6 +301,9 @@ type reactiveHandler struct {
 	// load-bearing math (same posture as the presence thresholds). Set by
 	// wireVoice from VoiceConfig; default 0.55.
 	queryMinScore float64
+	// queryMinMargin — the second half of that gate: how far the top hit must
+	// beat the runner-up. 0 ⇒ margin off.
+	queryMinMargin float64
 
 	// timeParser — used as a fallback for stage-0 reminder grammar matches
 	// (where the extractor didn't run). Shared with the router's extractor.
@@ -760,18 +764,23 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
 			log.Printf("voice: query notes: %v", err)
 			return "не получилось найти ответ."
 		}
-		// Confidence gate: below threshold, say "I don't know" rather than read
-		// back the least-unrelated note — a confident wrong recall is worse than
-		// a gap (spec's "not a guesser-of-truth"). Same instinct as the loop's
-		// since(key)==null → don't fire. Tuned for the ONNX embedder; the Hash
-		// floor scores lexically and may rarely clear it.
-		if len(notes) == 0 || notes[0].Score < h.queryMinScore {
+		// Confidence gate: below it, say "I don't know" rather than read back
+		// the least-unrelated note — a confident wrong recall is worse than a
+		// gap (spec's "not a guesser-of-truth"). Same instinct as the loop's
+		// since(key)==null → don't fire. Two parts: an absolute cosine floor,
+		// and a margin over the runner-up, which is the part that works with
+		// the e5 embedder's narrow score band. See memory.Confident.
+		noteScores := make([]float64, len(notes))
+		for i, n := range notes {
+			noteScores[i] = n.Score
+		}
+		if !memory.ConfidentScores(noteScores, h.queryMinScore, h.queryMinMargin) {
 			// Long-term memory recall (notes + facts) before general knowledge:
 			// the notes table can't answer fact questions, but the memory store
 			// indexes both. Only runs when notes-RAG already gave up → additive.
 			if h.memStore != nil {
 				if hits, herr := h.memStore.Search(ctx, vec, 3); herr == nil {
-					if text, ok := bestRecall(hits, h.queryMinScore); ok {
+					if text, ok := bestRecall(hits, h.queryMinScore, h.queryMinMargin); ok {
 						return text
 					}
 				}

deploy/mavend.json

@@ -40,7 +40,9 @@
       "tokenizer_path": "/opt/maven/models/embedder/multilingual-e5-small/tokenizer.json",
       "lib_path": "/opt/maven/lib/libonnxruntime.so"
     },
-    "llm_router": false,
+    "llm_router": true,
+    "query_min_score": 0.55,
+    "query_min_margin": 0.008,
     "tool_timeout": "30s",
     "tools": [
       { "name": "status",     "cmd": ["systemctl", "status"],         "scope": "homelab", "destructive": false },

internal/config/config.go

@@ -258,18 +258,25 @@ type VoiceConfig struct {
 	RouterThreshold float64 `json:"router_threshold,omitempty"`
 
 	// LLMRouter — route with the resident model instead of the embedding
-	// classifier. Measured on the held-out fixture (ROUTING-EVAL-31-07-2026.md)
-	// the model gets 50.0% of intents right against the classifier's 36.8%, but
-	// it costs about 800ms per turn instead of 30ms.
+	// classifier. On by default since Vikunja #320.
 	//
-	// 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.
+	// Measured on the held-out fixture (ROUTING-EVAL-31-07-2026.md): 63.2% of
+	// intents right against the classifier's 50.0%, and no route errors. It
+	// costs about 1s per turn instead of 30ms.
 	//
-	// 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"`
+	// It is safe to leave on. The model can refuse — it answers "unknown" when
+	// it cannot route, and the turn drops to the classifier and its clarify
+	// gate. Any LLM error does the same, so a turn never breaks on the model.
+	// Slot extraction runs on LLM decisions too, so acts get their Fn and
+	// reminders their Time.
+	//
+	// Set it false to go back to the classifier, e.g. on a box with no
+	// llama-server or when 1s a turn is too slow.
+	//
+	// It is a pointer so that "missing from the file" and "explicitly false"
+	// are different things: missing means on, false means off. Read it with
+	// UseLLMRouter(), not directly.
+	LLMRouter *bool `json:"llm_router,omitempty"`
 
 	// QueryMinScore — the note-recall confidence gate. Top cosine below this
 	// ⇒ "I don't know" instead of a guess. Tuned for the ONNX embedder (0.55);
@@ -277,6 +284,14 @@ type VoiceConfig struct {
 	// default if unset.
 	QueryMinScore float64 `json:"query_min_score,omitempty"`
 
+	// QueryMinMargin — the second half of the recall gate: the top hit must
+	// beat the runner-up by more than this. The absolute score above cannot do
+	// the job on its own, because the e5 embedder puts every cosine in one
+	// narrow high band, so a made-up question scores as high as a real one.
+	// The margin asks whether one note is clearly the best instead.
+	// Negative ⇒ off. 0 ⇒ the default below.
+	QueryMinMargin float64 `json:"query_min_margin,omitempty"`
+
 	// Persona — optional prompt prefix that tunes maven's character. Prepended
 	// to every LLM system prompt (nudge phrasing, note queries, general
 	// knowledge). Empty string ⇒ current hardcoded persona (feminine-gendered
@@ -404,7 +419,14 @@ const (
 	DefaultAutotuneInterval = 10 * time.Minute
 	DefaultRouterThreshold  = 0.55
 	DefaultQueryMinScore    = 0.55
-	DefaultToolTimeout      = 30 * time.Second
+	// Read off the margin sweep in internal/memory/recalleval on the e5
+	// embedder: 0.008 answers 68% of real questions (down from 72%) and cuts
+	// false recall from 5/5 to 1/5. Every larger delta costs real recall
+	// without removing that last one until 0.020, which drops recall to 44%.
+	DefaultQueryMinMargin = 0.008
+	DefaultToolTimeout    = 30 * time.Second
+	// DefaultLLMRouter — route with the resident model unless told otherwise.
+	DefaultLLMRouter = true
 
 	DefaultFactEnrichmentInterval = 30 * time.Second
 )
@@ -486,9 +508,21 @@ func (c *Config) applyDefaults() {
 		if c.Voice.QueryMinScore <= 0 {
 			c.Voice.QueryMinScore = DefaultQueryMinScore
 		}
+		// Unset ⇒ default. Negative is how you turn the margin off on purpose,
+		// so it is clamped to 0 rather than replaced by the default.
+		switch {
+		case c.Voice.QueryMinMargin == 0:
+			c.Voice.QueryMinMargin = DefaultQueryMinMargin
+		case c.Voice.QueryMinMargin < 0:
+			c.Voice.QueryMinMargin = 0
+		}
 		if c.Voice.ToolTimeout <= 0 {
 			c.Voice.ToolTimeout = Duration(DefaultToolTimeout)
 		}
+		if c.Voice.LLMRouter == nil {
+			on := DefaultLLMRouter
+			c.Voice.LLMRouter = &on
+		}
 	}
 
 	// routines: default severity to care-class (1) — the safe floor: a
@@ -507,6 +541,15 @@ func (c *Config) applyDefaults() {
 	}
 }
 
+// UseLLMRouter reports whether to route with the resident model. Unset means
+// on; only an explicit false in the config turns it off.
+func (v *VoiceConfig) UseLLMRouter() bool {
+	if v == nil || v.LLMRouter == nil {
+		return DefaultLLMRouter
+	}
+	return *v.LLMRouter
+}
+
 func (c *Config) validate() error {
 	if c.Phraser != nil {
 		if c.Phraser.ModelPath == "" {

internal/config/config_test.go

@@ -171,14 +171,26 @@ func TestWeatherConfigNilOK(t *testing.T) {
 	}
 }
 
-func TestLLMRouterDefaultsOff(t *testing.T) {
+func TestLLMRouterDefaultsOn(t *testing.T) {
 	p := writeConfig(t, `{"voice":{"enabled":true,"bind":"127.0.0.1:9100"}}`)
 	c, err := Load(p)
 	if err != nil {
 		t.Fatalf("Load: %v", err)
 	}
-	if c.Voice.LLMRouter {
-		t.Error("voice.llm_router absent should mean false")
+	if !c.Voice.UseLLMRouter() {
+		t.Error("voice.llm_router absent should mean on")
+	}
+}
+
+// Missing and explicitly false must not mean the same thing.
+func TestLLMRouterExplicitFalseTurnsItOff(t *testing.T) {
+	p := writeConfig(t, `{"voice":{"enabled":true,"bind":"127.0.0.1:9100","llm_router":false}}`)
+	c, err := Load(p)
+	if err != nil {
+		t.Fatalf("Load: %v", err)
+	}
+	if c.Voice.UseLLMRouter() {
+		t.Error("voice.llm_router false should turn it off")
 	}
 }
 
@@ -188,7 +200,7 @@ func TestLLMRouterRead(t *testing.T) {
 	if err != nil {
 		t.Fatalf("Load: %v", err)
 	}
-	if !c.Voice.LLMRouter {
+	if !c.Voice.UseLLMRouter() {
 		t.Error("voice.llm_router true was not read")
 	}
 }

internal/memory/gate.go

@@ -0,0 +1,38 @@
+package memory
+
+// Confidence gate for a recall. Two checks, both must pass before Maven says a
+// note back:
+//
+//   - minScore — an absolute cosine floor.
+//   - minMargin — the top hit must beat the runner-up by more than this.
+//
+// The margin is the one that carries the weight. The e5 embedder packs every
+// score into a narrow high band (0.79-0.89 on the recall fixture), so an
+// absolute floor cannot tell a real hit from a confident-looking miss: every
+// value under the band admits everything, every value above it answers nothing.
+// A margin asks a different question — "is this note clearly the best one, or
+// is the whole shelf equally close?" — and a made-up question has no clear best.
+//
+// With one hit and no runner-up there is nothing to compare, so only the floor
+// applies.
+
+// ConfidentScores reports whether the top score clears both gates. scores must
+// be sorted highest first. minMargin <= 0 turns the margin check off.
+func ConfidentScores(scores []float64, minScore, minMargin float64) bool {
+	if len(scores) == 0 || scores[0] < minScore {
+		return false
+	}
+	if minMargin > 0 && len(scores) > 1 && scores[0]-scores[1] <= minMargin {
+		return false
+	}
+	return true
+}
+
+// Confident is ConfidentScores for search results.
+func Confident(results []Result, minScore, minMargin float64) bool {
+	scores := make([]float64, len(results))
+	for i, r := range results {
+		scores[i] = r.Score
+	}
+	return ConfidentScores(scores, minScore, minMargin)
+}

internal/memory/gate_test.go

@@ -0,0 +1,45 @@
+package memory
+
+import "testing"
+
+func TestConfidentScores(t *testing.T) {
+	cases := []struct {
+		name      string
+		scores    []float64
+		minScore  float64
+		minMargin float64
+		want      bool
+	}{
+		{"no hits", nil, 0.55, 0.008, false},
+		{"below the floor", []float64{0.40, 0.10}, 0.55, 0.008, false},
+		{"clear winner", []float64{0.86, 0.70}, 0.55, 0.008, true},
+		{"runner-up too close", []float64{0.860, 0.858}, 0.55, 0.008, false},
+		// The rule is "beats the runner-up by MORE than delta". Not testing an
+		// exactly-equal margin: no pair of these decimals subtracts to exactly
+		// 0.008 in binary float, so such a test would pin rounding, not the rule.
+		{"margin just under delta", []float64{0.8079, 0.8}, 0.55, 0.008, false},
+		{"margin just over delta", []float64{0.8081, 0.8}, 0.55, 0.008, true},
+		// One hit: nothing to compare against, so only the floor applies.
+		{"single hit clears", []float64{0.86}, 0.55, 0.008, true},
+		{"single hit below floor", []float64{0.10}, 0.55, 0.008, false},
+		// Margin off — the old absolute-only behaviour.
+		{"margin off admits a tie", []float64{0.86, 0.86}, 0.55, 0, true},
+	}
+	for _, c := range cases {
+		t.Run(c.name, func(t *testing.T) {
+			if got := ConfidentScores(c.scores, c.minScore, c.minMargin); got != c.want {
+				t.Errorf("got %v, want %v", got, c.want)
+			}
+		})
+	}
+}
+
+func TestConfidentReadsResultScores(t *testing.T) {
+	res := []Result{{ID: "a", Score: 0.86}, {ID: "b", Score: 0.858}}
+	if Confident(res, 0.55, 0.008) {
+		t.Error("thin margin passed the gate")
+	}
+	if !Confident(res, 0.55, 0) {
+		t.Error("margin off should fall back to the floor alone")
+	}
+}

internal/memory/recalleval/recalleval.go

@@ -177,6 +177,8 @@ type Outcome struct {
 	Tied    bool
 	TopID   string
 	TopScor float64
+	// Margin — top1 − top2. 0 when fewer than two hits came back.
+	Margin  float64
 	Reasons []string
 }
 
@@ -184,8 +186,10 @@ type Outcome struct {
 // ranks first but is silenced by query_min_score is a threshold problem, and a
 // note that never ranks first is an embedder problem. Those are different fixes.
 type Report struct {
-	Name       string
-	MinScore   float64
+	Name     string
+	MinScore float64
+	// MinMargin — how far the top hit must beat the runner-up. 0 ⇒ off.
+	MinMargin  float64
 	Total      int
 	Answerable int
 	Rank1      int
@@ -212,9 +216,14 @@ type Report struct {
 	// where the right note ranked first, and for the no-answer cases. The gap
 	// between these two distributions is what a defensible query_min_score
 	// would have to sit inside; if they overlap, no threshold separates them.
-	CorrectTop    []float64
-	NoAnswerTop   []float64
-	P50, P95, Max time.Duration
+	CorrectTop  []float64
+	NoAnswerTop []float64
+	// CorrectMargin / NoAnswerMargin — the same two groups, but top1 − top2
+	// instead of top1. This is the pair the margin gate has to separate, and
+	// unlike the absolute scores it is what the sweep reads.
+	CorrectMargin  []float64
+	NoAnswerMargin []float64
+	P50, P95, Max  time.Duration
 }
 
 // TagStat — passed/total for one slice of the fixture.
@@ -245,13 +254,14 @@ func ratio(n, d int) float64 {
 // the run on an embed or search error: an erroring case scores as a miss and is
 // counted in Errors, because "the embedder was down" and "the embedder was
 // wrong" are different numbers.
-func Score(ctx context.Context, name string, emb router.Embedder, newStore NewStore, minScore float64, f Fixture) (Report, error) {
+func Score(ctx context.Context, name string, emb router.Embedder, newStore NewStore, minScore, minMargin float64, f Fixture) (Report, error) {
 	rep := Report{
-		Name:     name,
-		MinScore: minScore,
-		Total:    len(f.Cases),
-		ByTag:    map[string]TagStat{},
-		ByLang:   map[string]TagStat{},
+		Name:      name,
+		MinScore:  minScore,
+		MinMargin: minMargin,
+		Total:     len(f.Cases),
+		ByTag:     map[string]TagStat{},
+		ByLang:    map[string]TagStat{},
 	}
 	lat := make([]time.Duration, 0, len(f.Cases))
 
@@ -261,7 +271,7 @@ func Score(ctx context.Context, name string, emb router.Embedder, newStore NewSt
 		} else {
 			rep.NoAnswer++
 		}
-		o, err := scoreCase(ctx, emb, newStore, minScore, c, f.Filler)
+		o, err := scoreCase(ctx, emb, newStore, minScore, minMargin, c, f.Filler)
 		if err != nil {
 			return Report{}, err
 		}
@@ -285,6 +295,11 @@ func Score(ctx context.Context, name string, emb router.Embedder, newStore NewSt
 			} else if !o.Rank1 {
 				rep.WrongTop++
 			}
+			if o.Rank1 {
+				// Margins are collected on rank, not on the gate, so the
+				// distribution does not move as the sweep changes the gate.
+				rep.CorrectMargin = append(rep.CorrectMargin, o.Margin)
+			}
 			if o.Rank1 && o.Recalled != "" {
 				rep.CorrectTop = append(rep.CorrectTop, o.TopScor)
 			}
@@ -293,6 +308,7 @@ func Score(ctx context.Context, name string, emb router.Embedder, newStore NewSt
 				rep.FalseRecall++
 			}
 			rep.NoAnswerTop = append(rep.NoAnswerTop, o.TopScor)
+			rep.NoAnswerMargin = append(rep.NoAnswerMargin, o.Margin)
 		}
 
 		if o.Pass {
@@ -307,6 +323,8 @@ func Score(ctx context.Context, name string, emb router.Embedder, newStore NewSt
 
 	sort.Float64s(rep.CorrectTop)
 	sort.Float64s(rep.NoAnswerTop)
+	sort.Float64s(rep.CorrectMargin)
+	sort.Float64s(rep.NoAnswerMargin)
 	sort.Slice(lat, func(i, j int) bool { return lat[i] < lat[j] })
 	rep.P50, rep.P95 = percentile(lat, 0.50), percentile(lat, 0.95)
 	if len(lat) > 0 {
@@ -318,7 +336,7 @@ func Score(ctx context.Context, name string, emb router.Embedder, newStore NewSt
 // scoreCase inserts the case's notes into a fresh store, then runs the read
 // path the daemon runs. The returned error is fatal (the harness is broken);
 // an embedder or store failure on the query lands in Outcome.Err instead.
-func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minScore float64, c Case, filler []StoredNote) (Outcome, error) {
+func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minScore, minMargin float64, c Case, filler []StoredNote) (Outcome, error) {
 	st, release, err := newStore()
 	if err != nil {
 		return Outcome{}, fmt.Errorf("%s: new store: %w", c.ID, err)
@@ -357,7 +375,10 @@ func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minS
 
 	if len(hits) > 0 {
 		o.TopID, o.TopScor = hits[0].ID, hits[0].Score
-		o.Recalled = bestRecall(hits, minScore)
+		if len(hits) > 1 {
+			o.Margin = hits[0].Score - hits[1].Score
+		}
+		o.Recalled = bestRecall(hits, minScore, minMargin)
 	}
 	for i, h := range hits {
 		if h.ID != c.Want {
@@ -378,14 +399,14 @@ func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minS
 	switch {
 	case !c.Answerable():
 		if o.Recalled != "" {
-			o.Reasons = append(o.Reasons, fmt.Sprintf("false recall: %q at %.3f, want silence", o.TopID, o.TopScor))
+			o.Reasons = append(o.Reasons, fmt.Sprintf("false recall: %q at %.3f (margin %.3f), want silence", o.TopID, o.TopScor, o.Margin))
 		}
 	case o.Tied:
 		o.Reasons = append(o.Reasons, fmt.Sprintf("tie at %.3f — the right note is on top only by sort order", o.TopScor))
 	case !o.Rank1:
 		o.Reasons = append(o.Reasons, fmt.Sprintf("top hit %q (%.3f), want %q%s", o.TopID, o.TopScor, c.Want, rankNote(o.Rank3)))
 	case o.Recalled == "":
-		o.Reasons = append(o.Reasons, fmt.Sprintf("right note ranked first but scored %.3f < gate %.2f — daemon says \"не знаю\"", o.TopScor, minScore))
+		o.Reasons = append(o.Reasons, fmt.Sprintf("right note ranked first at %.3f (margin %.3f) but the gate silenced it — daemon says \"не знаю\"", o.TopScor, o.Margin))
 	}
 	o.Pass = len(o.Reasons) == 0
 	return o, nil
@@ -401,8 +422,8 @@ func rankNote(inTop3 bool) string {
 // bestRecall mirrors cmd/mavend/recall.go — the gate the daemon actually
 // applies to a memory hit. Duplicated rather than imported because package main
 // is not importable; recalleval_test.go asserts the two agree in behaviour.
-func bestRecall(results []memory.Result, min float64) string {
-	if len(results) == 0 || results[0].Score < min {
+func bestRecall(results []memory.Result, minScore, minMargin float64) string {
+	if !memory.Confident(results, minScore, minMargin) {
 		return ""
 	}
 	return results[0].Meta["text"]
@@ -437,7 +458,7 @@ func percentile(sorted []time.Duration, p float64) time.Duration {
 // the slices that name where the path is weak.
 func (r Report) String() string {
 	var b strings.Builder
-	fmt.Fprintf(&b, "%s: %d/%d cases pass (gate %.2f)\n", r.Name, r.Passed, r.Total, r.MinScore)
+	fmt.Fprintf(&b, "%s: %d/%d cases pass (gate %.2f, margin %.3f)\n", r.Name, r.Passed, r.Total, r.MinScore, r.MinMargin)
 	fmt.Fprintf(&b, "  recall@1 %.1f%% (%d/%d)  recall@3 %.1f%% (%d/%d)  answered after gate %.1f%% (%d/%d)\n",
 		100*r.Recall1(), r.Rank1, r.Answerable,
 		100*r.Recall3(), r.Rank3, r.Answerable,
@@ -448,6 +469,8 @@ func (r Report) String() string {
 		100*r.FalseRecallRate(), r.FalseRecall, r.NoAnswer)
 	fmt.Fprintf(&b, "  top-1 score, right note first: %s\n", spread(r.CorrectTop))
 	fmt.Fprintf(&b, "  top-1 score, must be silent:   %s\n", spread(r.NoAnswerTop))
+	fmt.Fprintf(&b, "  margin top1-top2, right note first: %s\n", spread(r.CorrectMargin))
+	fmt.Fprintf(&b, "  margin top1-top2, must be silent:  %s\n", spread(r.NoAnswerMargin))
 	fmt.Fprintf(&b, "  latency: p50 %s  p95 %s  max %s\n", r.P50, r.P95, r.Max)
 	fmt.Fprintf(&b, "  by lang: %s\n", renderStats(r.ByLang))
 	fmt.Fprintf(&b, "  by tag:  %s\n", renderStats(r.ByTag))

internal/memory/recalleval/recalleval_test.go

@@ -142,21 +142,40 @@ func words(s string) []string {
 // cmd/mavend/recall.go (package main is not importable). This pins the copy to
 // the original's three rules: no hits, below the gate, or no text ⇒ silence.
 func TestBestRecallMatchesDaemon(t *testing.T) {
-	if got := bestRecall(nil, 0.55); got != "" {
+	if got := bestRecall(nil, 0.55, 0); got != "" {
 		t.Errorf("no hits: got %q, want silence", got)
 	}
 	low := []memory.Result{{ID: "a", Score: 0.4, Meta: map[string]string{"text": "чай"}}}
-	if got := bestRecall(low, 0.55); got != "" {
+	if got := bestRecall(low, 0.55, 0); got != "" {
 		t.Errorf("below gate: got %q, want silence", got)
 	}
 	noText := []memory.Result{{ID: "a", Score: 0.9, Meta: map[string]string{}}}
-	if got := bestRecall(noText, 0.55); got != "" {
+	if got := bestRecall(noText, 0.55, 0); got != "" {
 		t.Errorf("no text: got %q, want silence", got)
 	}
 	ok := []memory.Result{{ID: "a", Score: 0.9, Meta: map[string]string{"text": "чай"}}}
-	if got := bestRecall(ok, 0.55); got != "чай" {
+	if got := bestRecall(ok, 0.55, 0); got != "чай" {
 		t.Errorf("above gate: got %q, want %q", got, "чай")
 	}
+	// Margin: a close runner-up means the embedder cannot tell the two apart,
+	// so Maven stays silent even though both clear the absolute floor.
+	close := []memory.Result{
+		{ID: "a", Score: 0.86, Meta: map[string]string{"text": "чай"}},
+		{ID: "b", Score: 0.85, Meta: map[string]string{"text": "кофе"}},
+	}
+	if got := bestRecall(close, 0.55, 0.03); got != "" {
+		t.Errorf("thin margin: got %q, want silence", got)
+	}
+	if got := bestRecall(close, 0.55, 0); got != "чай" {
+		t.Errorf("margin off: got %q, want %q", got, "чай")
+	}
+	clear := []memory.Result{
+		{ID: "a", Score: 0.86, Meta: map[string]string{"text": "чай"}},
+		{ID: "b", Score: 0.70, Meta: map[string]string{"text": "кофе"}},
+	}
+	if got := bestRecall(clear, 0.55, 0.03); got != "чай" {
+		t.Errorf("wide margin: got %q, want %q", got, "чай")
+	}
 }
 
 // TestHashRecallBaseline — the CI ratchet. HashEmbedder, so it needs no model
@@ -171,7 +190,7 @@ func TestHashRecallBaseline(t *testing.T) {
 		t.Fatalf("Load: %v", err)
 	}
 	rep, err := Score(context.Background(), "recall+hash", router.NewHashEmbedder(hashDim), InMemory,
-		config.DefaultQueryMinScore, f)
+		config.DefaultQueryMinScore, config.DefaultQueryMinMargin, f)
 	if err != nil {
 		t.Fatalf("Score: %v", err)
 	}
@@ -201,11 +220,11 @@ func TestPersistentStoreScoresTheSame(t *testing.T) {
 		t.Fatalf("Load: %v", err)
 	}
 	emb := router.NewHashEmbedder(hashDim)
-	inMem, err := Score(context.Background(), "recall+hash+memory", emb, InMemory, config.DefaultQueryMinScore, f)
+	inMem, err := Score(context.Background(), "recall+hash+memory", emb, InMemory, config.DefaultQueryMinScore, config.DefaultQueryMinMargin, f)
 	if err != nil {
 		t.Fatalf("Score in-memory: %v", err)
 	}
-	persistent, err := Score(context.Background(), "recall+hash+sqlite", emb, sqliteStores(t), config.DefaultQueryMinScore, f)
+	persistent, err := Score(context.Background(), "recall+hash+sqlite", emb, sqliteStores(t), config.DefaultQueryMinScore, config.DefaultQueryMinMargin, f)
 	if err != nil {
 		t.Fatalf("Score sqlite: %v", err)
 	}
@@ -263,14 +282,16 @@ func TestONNXRecall(t *testing.T) {
 	if err != nil {
 		t.Fatalf("Load: %v", err)
 	}
-	rep, err := Score(context.Background(), "recall+onnx", emb, InMemory, config.DefaultQueryMinScore, f)
+	rep, err := Score(context.Background(), "recall+onnx", emb, InMemory, config.DefaultQueryMinScore, config.DefaultQueryMinMargin, f)
 	if err != nil {
 		t.Fatalf("Score: %v", err)
 	}
 	t.Log("\n" + rep.String() + rep.Failures())
-	// Cached for the sweep only: the headline run above must pay the real
+	// Cached for the sweeps only: the headline run above must pay the real
 	// embedder cost so its latency numbers mean something.
-	t.Log("\ngate sweep:\n" + sweep(t, Cache(emb), f))
+	cached := Cache(emb)
+	t.Log("\ngate sweep (margin off):\n" + sweep(t, cached, f))
+	t.Log("\nmargin sweep (gate 0.55):\n" + marginSweep(t, cached, f))
 }
 
 // sweep scores the fixture at a range of gates and renders one line each. Two
@@ -281,7 +302,7 @@ func sweep(t *testing.T, emb router.Embedder, f Fixture) string {
 	t.Helper()
 	var b strings.Builder
 	for _, gate := range []float64{0.0, 0.30, 0.40, 0.50, 0.55, 0.60, 0.70, 0.80, 0.90} {
-		rep, err := Score(context.Background(), "sweep", emb, InMemory, gate, f)
+		rep, err := Score(context.Background(), "sweep", emb, InMemory, gate, 0, f)
 		if err != nil {
 			t.Fatalf("sweep at %.2f: %v", gate, err)
 		}
@@ -290,3 +311,21 @@ func sweep(t *testing.T, emb router.Embedder, f Fixture) string {
 	}
 	return b.String()
 }
+
+// marginSweep is the same idea for the margin gate (top1 − top2 > delta), with
+// the absolute gate held at its default. The absolute score cannot separate a
+// real hit from a made-up question under e5 — every score lands in one narrow
+// band — so this sweep is the one that picks a number.
+func marginSweep(t *testing.T, emb router.Embedder, f Fixture) string {
+	t.Helper()
+	var b strings.Builder
+	for _, d := range []float64{0, 0.002, 0.005, 0.008, 0.01, 0.012, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.06} {
+		rep, err := Score(context.Background(), "margin sweep", emb, InMemory, config.DefaultQueryMinScore, d, f)
+		if err != nil {
+			t.Fatalf("margin sweep at %.3f: %v", d, err)
+		}
+		fmt.Fprintf(&b, "  delta %.3f: answered %d/%d (%.0f%%)  false recall %d/%d\n",
+			d, rep.Rank1-rep.Gated, rep.Answerable, 100*rep.Answered(), rep.FalseRecall, rep.NoAnswer)
+	}
+	return b.String()
+}

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, fmt.Sprintf("Severity: %s", sevLabel(c.Severity)))
-
+	ctxParts = append(ctxParts, "Ситуация: "+ruleTopic(c.Rule.Name))
+	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(
-		`Generate a nudge message. Context:
-%s
-
-Respond as JSON: {"response": "...", "mood": "..."}`,
-		strings.Join(ctxParts, "\n"),
-	)
+	return strings.Join(ctxParts, "\n") + "\n\n" + tail
 }
 
 type responseMood struct {