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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), 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