The Destructive column was a mechanism with no policy behind it: nothing said
which acts are destructive, whether a confirmed act stays confirmed, or what a
new tool domain inherits, so each domain answered for itself.
Three tiers, derived from the row rather than stored, so the answer can be
argued with in one place instead of being whatever the last person to tick the
checkbox believed. Safe runs. Destructive costs a confirm turn, every time —
a confirmation binds one capability, one target and one argument list, and it
dies with the parked turn. Irreversible is refused: a confirm turn there would
be theatre, because the STT, the router and the fuzzy allowlist match are all
guesses and a spoken "да" checks none of them. She names the gap; the row
stays enabled.
An unrecognised dispatch shape inherits destructive, not safe. A domain argues
its way down to running freely, never up to being gated.
An add and a crossing-off run at the top of actionNote, next to task
capture and before the embedding is paid for; the read-back is a query
source sitting beside "tasks", so the recall pass cannot answer "что мне
купить?" from an old note about the shop.
Crossing off one item claims the turn only when the list actually holds
that item, which is what keeps "купил новый ноутбук" a note.
These read h.dataStore rather than the CoreAPI: a list is local to the core
and nothing outside it writes one. The ipc seam is what it grows through
when something outside mavend needs to add to a list.
Same posture as task capture and for the same reason: the intent enum is a
contract shared with the relabelling prompt, so a list is not an eighth
intent. It is a note-shaped or query-shaped utterance carrying an explicit
marker, and the marker is a lookup.
The markers are deliberately explicit — "молоко закончилось" is an
observation and stays a note. The list tag is matched by stem, because
Russian declines it: "список покупок", "в покупки" and "в покупках" are one
list. ListGrammars puts both halves at stage 0, so an add and a read-back
never depend on the model having a good turn.
A list is a standing set of short strings under a tag. Not a task, because
milk is not work and the prioritiser must not count it as an errand; not a
fact, because it claims nothing. Nothing predicates over it, so two people
adding to the same list at once costs nothing.
Migration #19, plus AddListItem, ListItems, SetListItemStatus and ClearList.
The live-only unique index is the tasks one, per list: молоко twice before
the shop is one row, молоко again after it was crossed off is a new one.
The checks stay in the eval package and the daemon calls three of them:
feminine, address, and a new leaked-reasoning test. No retry — it doubles
the latency on the turn that is already going badly, and on the nudge path
the moment has passed. A failure falls back to the deterministic floor and
is logged with the whole rejected text and counted by check name.
hisgender is deliberately not run: the simulator showed it rejecting
"записала, что ты выпил воды", which is her own correct self-reference.
safeKey kept ASCII only, so "Встреча с Аней" and "Обед с мамой" both
reduced to "--" and shared one key on one day. The second event of the
day overwrote the first, silently, and his calendar is Russian.
Letters and digits in any script now pass. Migration #18 deletes the rows
written under the old rule instead of rewriting them: a calendar fact is
derived, the next poll writes the day again, and a stale row reads as an
extra meeting.
"какие планы на сегодня" worked and "какие планы на завтра" answered
"пока не умею": the agenda rule needs "у меня" or a calendar noun, and
that phrasing carries neither. "когда планёрка?" had the same shape.
Two rules. One takes a plan noun aimed at a named day, one takes a closed
list of event nouns after "когда"/"во сколько". Both route intent only,
so the query chain still decides which source answers.
classifier+onnx over the fixture: 55/79, 69.6% full, with the two new
cases passing and no case moving the other way.
Slots.Text was the raw utterance for every intent, so a reminder could not
have an empty subject. StillMissing never reported SlotText, the question
"О чём напомнить?" was unaskable, and the branch in PendingQuestion.Answer
that fills a text slot could only overwrite the whole request.
The LLM path now keeps the model's own text, empty included, and the gate
turns a subjectless reminder into a question. The classifier path is
unchanged: it has no subject parser, so the utterance is the only signal it
has.
llama-server aborts inside its own static teardown on SIGTERM — the
handler calls exit(), stream_session_manager's destructor throws, and the
process dies "signal: aborted (core dumped)". mavgpud sends that signal on
every eviction, so a routine yield wrote a multi-gigabyte core into
systemd-coredump and logged the same line a real crash would.
LimitCORE=0 in the unit stops the disk cost. A yielding flag, set by stop
and cleared by start, makes the log distinguish the two: only an exit we
did not ask for is still reported as an exit.
Not filed upstream. Searched ggml-org/llama.cpp for
"ggml_uncaught_exception" with SIGTERM and for stream_session_manager and
found nothing matching, so the issue still wants writing — by someone with
an account on that tracker, which is why it is not in this commit.
Nine reply cases and a fourth column in the talk report. The reply path is a
separate object from the phraser in the daemon, so Pair joins a Talker and a
Confirmer for a run that covers everything Maven says.
Cases carry intent/key/value because the replier is phrased from the decision the
router resolved, not from the raw utterance. Three of them are baits the other
paths cannot produce: a masculine verb about himself that she must not copy onto
herself, a polite plural input that must still come back на ты, and an unresolved
note that invites a question a confirmation is not allowed to ask.
Not scored against a model here — this box has no llama-server, and the baseline
test is opt-in on MAVEN_LLM_URL.
The prompt, the call and the output parsing now live in internal/phraser. What is
left here is the one thing the daemon adds: a clarify, a model error and an
unusable generation all answer from voice.StubReplier, so a turn never breaks on
the model. The duplicated stripThink and parseResponseMood copies are gone;
capture.go uses phraser.StripThink.
llmReplier lived in cmd/mavend, so the confirmation he hears after every fact,
note and reminder was the one phrasing path nothing could import or score.
Replier owns the prompt, the call and the parsing, and returns its errors instead
of hiding them — a dead model shows up as an error rather than as bad phrasing.
It has no stub fallback of its own; the daemon keeps that. StripThink is exported
for the daemon's own model callers.
#496 asked to skip the veto when the question and the hit are in
different scripts, so an English question stops losing a Russian note.
Measured first: the fixture has no cross-language case, and en-hard-024
is an English question against an English note. Both proposed fixes are
no-ops.
What the veto actually does on the fixture, with the real embedder: it
costs en-hard-024 and buys ru-silent-029. Pass count is 22/32 either
way; false recall is 0/5 with it and 1/5 without. The two cases are one
lexical class, so no rule cheap enough for RecallAllowed separates them.
Accepts the loss and pins both sides in a test.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The forwarded log named the cause in one line: the prompt cache limit
defaults to 8192 MiB. llama-server saves the full KV state of every idle
slot it evicts, 112 kiB per token, so RSS climbed about 170MB per
distinct prompt until the deployed server held 7.9GB for a 1.1GB model.
Measured on homesrv today, uncapped versus `--cache-ram 512`: RSS
plateaus at 932MB from the fourth distinct prompt instead of climbing.
The task's leading guess was wrong. `-ngl 99` costs almost no RSS,
because RADV keeps device memory outside the process. Numbers and method
in docs/evals/2026-08-03-llama-prompt-cache.md.
`-c 4096` is untouched. The knob is `phraser.cache_ram_mib`, unset means
512, negative passes no flag for a llama-server too old to know it.
The deploy still runs the old image, so the box keeps its 8 GiB default
until mavend is rebuilt.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
mavend scraped the child's stderr for the listen line and threw every
other line away, and never piped its stdout at all. Nothing about the
resident model's memory was diagnosable from a running box: no buffer
sizes, no KV-cache layout, no offload lines, no prompt-cache limit.
Both streams now share one pipe and every line lands in mavend's log
with a `llama:` prefix. The last 12 startup lines are also kept and go
into the error when the server dies before it listens, because bare
"EOF" never named which allocation it choked on.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
"что я говорил про бэкапы?" is his data by definition, and nothing outside the
box has ever heard him say anything. The boundary matched possession words only,
so the question walked past it into SearXNG and came back answered out of a Habr
article about somebody else's backups.
A speech-verb marker class was written first and dropped. Russian gives every
verb a dozen surface forms and the "как я говорил, ..." preamble list has no end,
so each form the lexicon missed was one more question reaching the world, and a
missing verb looks exactly like no bug.
The boundary now embeds two frozen seed sets and scores the turn's own query
vector, already computed upstream, against both. Nearest side wins. The
possession markers stay as the offline floor for a handler with no embedder.
19/19 held-out utterances correct against multilingual-e5-small; see
docs/evals/2026-08-03-personal-boundary.md. The live probe on the deployed box is
not done.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Automatic rather than a flag, unlike -reembed: only voice-tapped facts are in
this index, so it is tens of embeddings rather than thousands of notes. And
waiting for an operator to know the repair exists is the failure being fixed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Every write-path fix leaves the rows already stored wrong, and a box in that
state looks fine: recall answers with the wrong text and nothing logs an error.
That is how the original poison survived four restarts.
RepairFactVectors resolves each fact vector against the fact it names,
re-embeds the ones whose text is stale, and deletes the voided, superseded and
orphaned ones. Marker-guarded and idempotent, so it runs once per box and a run
that dies partway is simply redone.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
queryMemory returns a fact's stored text verbatim, so the text the write path
indexed is what he hears. It was the utterance, which made recall of any
voice-tapped fact answer with the sentence he said: go_version = 1.20 was
indexed as "какая последняя версия языка Go?", and that question came back.
FactRecallText renders the fact instead, and the utterance stays in meta as
provenance. Correcting a value now drops the key's vectors the way voiding one
does, since the superseded value was still answering.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
queryMemory and queryNotes both gate on score alone, so both needed it. The
eval keeps its own copy of bestRecall — package main is not importable — and a
fixture that measures a weaker gate than the daemon runs flatters it, so the copy
moves in step and its test pins the new rule.
Measured on the held-out recall fixture with the real embedder: 17/32 cases pass
→ 22/32, false recall 1/5 → 0/5, answered after gate 18/27 → 17/27. The one true
recall lost is en-hard-024, an English question against a Russian note, where no
lexical test can help.
The score gate cannot separate the right note from an unrelated one: the
held-out fixture puts the right note at 0.791-0.890 and the must-be-silent cases
at 0.795-0.835, so a note about his slow network answered 'почему небо синее?'.
RecallAllowed adds a topic veto, and applies it only to a question that mentions
nothing of his. That restriction is the whole design: demanding a shared word of
every recall silenced four true recalls on the fixture to kill one false one,
because recall exists to find the note whose words he no longer remembers. A
question about his own life keeps the embedder as its only judge.
IntentFact used to persist whatever the model invented for a question-shaped
utterance, at confidence 1.00, and index it for recall under the question's own
text. Two such rows then claimed seven unrelated world questions and silently
disabled world answering.
A question now goes down the query chain, which is what he asked for. The second
half is confidence: a value grounded in what he said stays 1.00, a value the model
supplied for words he never said drops to 0.60 and says so in the log. Same
reasoning as 'LLM output is not authorization' on the act path.
The predicate a fact write needs before it trusts a routing decision. Tokenized,
not substring: 'что' inside 'чтобы' is not a question. Capture verbs win over
every question signal, because 'запиши что я пил воду' contains an interrogative
and is still a capture.
Revert voided the fact row and left the vector, so recall kept serving the
voided fact's utterance and the documented repair reported success on a box that
stayed broken. There was no way to repair a poisoned box at all.
DeletePrefix covers every vector for the key, earlier rows included: their values
are superseded, and a superseded value has no business claiming a turn. It is
best-effort — the audit trail is already committed, and a fact that is voided but
still recallable beats a void that failed.
Owner's correction. It is the same host CLAUDE.md already calls workpc, and
two names for one machine read as two machines. The dated eval file keeps the
old name: a measurement is never edited after the day it was taken.
The offload inventory grows a column, because "seven callers of the resident
model" stopped being the useful fact. Which of them is offloaded, and under
which half of the rule, is. Three are resident-only on purpose and the table
now says why rather than leaving it to be rediscovered.
The three-outcome table is the part that was not obvious from the rule as
written. A configured-and-asleep workstation names the gap; a box with no
workstation block does not, because naming a gap requires a gap.
queryGeneral has nothing fetched to fall back on, so it is the sharp case:
with a workstation configured and asleep he is told that, rather than told
something false in a confident voice. The 1.7B answering a world question is
where "Война и мир" got Левитан as its author.
The sources that already hold a passage — a live search, a ZIM article, a
page he named — go through the world model too, but read the passage back
when it is not there instead of naming a gap. A real quote beats "не могу
сейчас", and nothing is invented on either path.
The Stub and every test double keep the Phraser interface they have.
PhraseWorld is reached by assertion, and a phraser without it is the
no-workstation case.
The middle outcome is the whole task: a workstation that is configured and
asleep produces a gap, and the resident model is never asked. The parity
test compares the bytes PhraseWorld sends the workstation against the bytes
PhraseQuery sends the resident model, so the fixtures and the daemon cannot
measure two different prompts.
The nudge tests cover the silent half from both sides, including the
temperature, which is how the workstation would otherwise change how she
sounds without anyone deciding to.
The naming half of the degradation rule in docs/offload.md. PhraseWorld has
three outcomes: no workstation configured means the resident model answers
exactly as today, a workstation that is taking work answers, and one that is
asleep returns ErrNoWorldModel so the caller can say so. Naming a gap
requires a gap — on a box that never had a second model, refusing every
world question would remove a capability he has now.
Both prompts move into knowledgePrompt and evidencePrompt, shared by
PhraseQuery and PhraseWorld, because prompt parity across two models stops
holding the moment there are two copies of a prompt.
The silent half comes with it: chatWithSystem and chatWithMessages prefer
the workstation when it will take work, at the same 0.7 the resident
transport samples at, and say nothing when it will not. That covers the
digestion worker's nudge and reminder phrasing without touching tick.go.
Only Available and CompleteRemote are in the Remote interface. Pair.Complete
has its own floor and the phraser already owns one; two floors under a
single call is one too many.
The phraser's own transport has always sampled at 0.7 and this client has
always been greedy. Routing a phrasing call through the client must not
change how it decodes, so Req carries the temperature and 0 — the zero
value, and what every existing caller wanted — is still greedy.
Both fixtures, run from homesrv across the LAN with the proxy env stripped.
Routing: 84.4% full / 93.5% intent-only at p50 329ms through the cascade, against
72.7% / 77.9% at p50 0.80-1.04s for Qwen3-1.7B. Talk: 25/27 against 20/27, with
knowledge 9/9. Nudges 15/15. Settles #485's first assumption by measurement.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NJYcaBiuny9UGSpFweQVQ1
modelSeam builds an llm.Pair when a workstation is configured and hands it to
the router and the replier. Both are the silent half of the degradation rule:
the big model is only better there, and he is never told which model answered.
No block, no probe, and the box behaves exactly as it did.
claude
kami