The subjectless-reminder gate has been dead since V-383. It tested
`d.Slots.Text == ""`, and that slot is never empty: fillSlots hands it the
utterance when the model names nothing narrower. Measured on the box on
05-08-2026 — "напомни" alone routed to IntentReminder with Text:напомни,
reached actionReminder, and answered "не получилось разобрать время
напоминания." A parse error for a request he never finished asking about.
"ну напомни же" did the same.
The test is now what the slot CONTAINS. reminderHasSubject discounts the
reminder verb by lemma and the filler particles, and asks whether anything
is left. A day or an hour counts as a subject, which is why this does not
reuse cmd/mavend/reminderbody.go — that one strips the time words too.
filler_particles is the lexicon's 16th set. Not a stopword list: every word
in it is one that cannot BE a reminder's subject.
Measured against the 87-case fixture with and without the change: 65/87
both ways, identical clarify counts, because no case exercised the shape.
So amb-007 "напомни" and amb-008 "ну напомни же" were added, both
want_clarify. At 89 cases the cascade scores 67/89 (75.3% full, 79.8%
intent-only), 3 false clarifies / 1 missed, p50 1.199s — the two new cases
clarify, and nothing else moved. The classifier path still guesses both
(62/89, 8 missed clarify); the gate is on the LLM arm only.
The box needs a rebuild for this to take effect.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SoL7EBdYC5Mhz3DJd49GJy
CorrectMisroute has been in the router since it was written with no caller
outside a test. repair.go is the half that reads the words: a marker saying
she was wrong plus the intent it should have been, with the negated half
skipped, and it teaches the classifier and redoes the request under the
corrected intent.
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.
Confidence was hardcoded to 1.0 for every LLM decision, and the LLM branch
in Router.Route returned straight from fillSlots without ever touching the
stage-3 threshold gate — so the LLM path could not produce a Clarify no
matter what confidence a model reported. That is why all 6 want_clarify
cases in the 77-case RU fixture were missed by every model in the bake-off.
Fix reads structural signal instead of changing the (parity-locked) router
prompt: a single-token utterance ("вода", "бэкап") is flagged thin evidence
in llmrouter.go; a fact left keyless or an act that never resolves to an
allowlisted fn, checked after fillSlots so the deterministic parsers get
first crack, is flagged in router.go's new gateLLMDecision. Anything below
config.DefaultRouterThreshold (0.55) now sets Clarify=true through the same
path the classifier already uses.
Added unit tests with a stubbed Completer proving both directions: thin
cases clarify, clean multi-word/resolved-slot cases stay confident. The
77-case fixture re-run against a live llama-server is still needed to
confirm the 6/6 moves — not done here, no llama-server on this box.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
An LLM-routed reminder came back with no parsed time and an act with no
fn, because only the classifier path ran the extractor. Now the router
runs the same extraction after an LLM decision and fills only the empty
slots. No time in the utterance still means no time.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
The most load-bearing decision in the project was stated four incompatible
ways: the docs said Qwen3-1.7B, deploy/mavend.json said Qwen3.5-2B, the repo's
models/llm/ held an LFM2.5-1.2B gguf, and five code comments still said LFM.
Answering "which model is deployed" meant re-deriving it from scratch every
time.
Two facts the review missed, found while resolving it:
- /mnt/hdd1/llms is bind-mounted over /opt/maven/models/llm, which shadows the
repo's models/llm/. The LFM2.5 gguf sitting there was never loaded by
anything, so it was not evidence of the deployed model at all.
- That library holds Qwen3.5-0.8B, -2B and -4B, and no Qwen3-1.7B. The config
pointed at a file that does exist; the docs' Qwen3-1.7B was the stale claim,
the reverse of the assumed direction. Qwen3-1.7B is the CPT target, and that
training is still in flight (Vikunja #122), so no such gguf exists yet.
phraser.model_path moves to Qwen3.5-0.8B (Q4_K_M) — the smallest checkpoint on
disk, chosen for latency, and relevant to whether the LLM router is affordable
on this box. Docs and comments now say the same thing in one voice: 0.8B
resident now, CPT'd Qwen3-1.7B as the target, and the bind-mount shadowing
written down so the next reader does not mistake models/llm/ for ground truth.
Comments name the model, never a filename, so a swap stays a one-line config
change.
n_gpu_layers: 99 is correct and stays — compose passes /dev/dri and the render
gid for Vulkan offload to the Vega iGPU. CLAUDE.md's "CPU-only" was the stale
half of that contradiction and is corrected.
phraser.go also dropped a wrong "sub-1b, prompted not trained" size claim: the
target is trained end-to-end (RU CPT + joint persona/router SFT).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01X5JApcrCRVGmqrxnhynSik
- Add LLMRouter: grammar-constrained LFM call for intent classification
after stage-0, before classifier cascade. Errors fall through gracefully.
- Add IntentChat: conversational intent with no store side-effect, routed
through LLM -> phraser chat endpoint.
- Extract slots for Chat: no structured slots, full utterance is payload.
- Extend stage-0 grammars to fire through Cyrillic wake-word spellings
(Мэйвен/Мейвен/Майвен/etc.) produced by Russian STT model.
- StripWakeToken helper strips leading wake in any script so time/date
grammars still match when wake is present.
- Add classifier examples for chat utterances (EN + RU).
- Wire LLMRouter into Router.Config; optional, nil-safe.
claude
kami