One up-front LLM pass over the full transcript extracts a theme summary +
terminology map, merges it under the user's manual glossary (user entries
always win), caches it on the dub job per target language (job_data blob, no
schema change), and injects the brief into every per-segment prompt. A new
reflect pass then critiques each segment's direct translation for wordiness /
stiff register and rewrites it as natural spoken dialogue — any failure or
divergence silently keeps the direct translation. Both stages have Dub-tab
toggles (default ON for the LLM engine, persisted; MT engines unaffected),
with i18n strings across all 21 locales and docs updated.
Co-authored-by: mergetest <test@local>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
