---
id: two-stage-user-styled-translation/08-the-same-record-drives-the-learner-overlay
title: 'Two-stage styled translation exists only in pieces: 8. The same record drives
  the learner overlay'
report: https://ai.hiina.space/reports/two-stage-user-styled-translation.md
section: 8
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# Two-stage styled translation exists only in pieces: 8. The same record drives the learner overlay

- Section 8 of 12 of the report "Two-stage styled translation exists only in pieces" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/two-stage-user-styled-translation.md
- Previous: https://ai.hiina.space/reports/two-stage-user-styled-translation/07-a-concrete-record-design-and-where-the.md
- Next: https://ai.hiina.space/reports/two-stage-user-styled-translation/09-real-gaps-as-far-as-this-search.md

## 8. The same record drives the learner overlay

A companion report on Japanese learning and Anki tooling proposes a selective-gloss reading overlay with a five-rung hint ladder (highlight unknown lemma, reading, multiple-choice L1 gloss, contextual sense or grammar note, full translation) over a learner model keyed by JMdict entry ID with FSRS retrievability. The record gives that overlay its expensive rungs for free. Rung 5 is the neutral render, or a styled one for a learner who wants to read in a preferred voice. Rung 4, which the overlay design planned as a live call to a local 7-14B model with unmeasured latency, becomes a short render from `literal_en`, `register`, `address_terms` and the lemma's sense in context, all precomputed by a model that saw the whole script. The record's `lemmas` field, filled offline with Sudachi and JMdict, removes per-line tokenisation from the hot path and lets the overlay pre-compute which lines will need rung 4 for the learner's current known set.

The learner model then becomes one more conditioning input, alongside the style profile. For the overlay's clause-level hybrid-reading mode, the record's `clauses` alignment solves the problem the earlier design identified, that Japanese SOV order breaks word-level weaving, because swaps happen at clause boundaries the frontier pass already aligned. The earlier report's other finding still binds: prompted frontier models are poor at hitting low vocabulary levels, worse in Japanese (GPT-5.2 58.8% coverage against 76.0% for an RL-tuned 4B model) ([Re-RIGHT](https://arxiv.org/pdf/2604.05302)), so a learner render must be verified in code against the learner's known set and regenerated where it misses, exactly like the style post-check. Learner state changes daily, so raw retrievability should not go in the cache key. Key on a coarse bucket, such as the set of unknown lemmas in the line, and keep rungs 1-3 deterministic and uncached. The spoiler flag from VNDB glossaries also earns its keep here: a rung-4 note written by a model that has read the whole script can leak the ending unless the record bounds it.

---

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space