I built a voice-first Japanese tutor that listens, corrects, scores pitch accent, and remembers your mistakes. The pipeline works end to end. The evaluation says the correcting brain collapses to one error type out of four. Publishing the failure is the point.

Scope note, first paragraph as promised: 300 sentences from Tatoeba (CC-BY) with script-injected errors — no fluent reader checked any label, disclosed throughout; one local Swallow-8B model, greedy decoding; simulated mistake histories, never real learners; no LLM judge anywhere.

The thing I built

Kotoba-Tutor is a LangGraph agent: push-to-talk CLI → faster-whisper transcription → Swallow-8B correction analysis (structured JSON) → pitch-accent scoring with my earlier Akusento model → short explanation → a cross-session store counting your mistake types → a scheduler drilling your weakest one. The explanation is spoken back with a local TTS voice.…