BabyYarros: corpus built, gender resolution fixed, rename blocked on leak gate

Located the source: five Rebecca Yarros works in the Kvasir licensed library, with
rights recorded as gated. Built D1 at 208 chapters and 780,744 words, which is 15%
larger than the Brontë corpus. No unwrap step was needed because Kvasir's cleaner
already emits flowing paragraphs, so the hard-wrap defect that cost a re-cut on
Brontë does not exist here. The alphabet was re-derived rather than inherited: 23
non-ASCII letters across three forms, against F02's 4 on a smaller sample. Same
ASCII-fold conclusion from a different measurement, which is the reason to re-derive
per corpus.

The interesting finding is a new pathology. In a rotating first-person POV corpus,
every book's narrator gets the wrong gender. Measured against six names verified in
the text, the pronoun resolver called Violet male, Leah male and Landon female --
three of eighteen wrong, and all three are the narrator of the book where they were
misgendered. A narrator is "I" in her own book, so her name appears mostly inside
the other lead's dialogue surrounded by his pronouns. This is Brontë's "Jane called
male" amplified by rotating POV. Title-first resolution, which fixed it for Brontë,
is nearly blind here because contemporary romance uses given names rather than
honorifics. What works is the POV header: resolve each name from the chapters it
does not narrate. Validated at 9 correct, 9 held, 0 wrong against the previous 7, 8
and 3 wrong, and the instrument refuses to write unless it beats what it replaces.

Re-pointing rename.py surfaced three bugs, two of which would have silently
corrupted the corpus. Gender came only from honorifics and the entities file's
gender field was ignored, so the POV fix had no effect until wired through; that
took wilder from 1 gendered entity to 13. The pool labels were hardcoded in a print
statement, so any non-Brontë preset crashed. And the collision-filter log claimed
it dropped names colliding with Brontë entities regardless of which corpus it
filtered against -- the logic was right but the message named the wrong corpus,
which is how a reader later concludes the filter ran on the wrong thing.

D3 is blocked and nothing has been trained. The leak gate shows 86 of 232
renameable source entities surviving where the Brontë run reached 0 of 203. It
decomposes into detector false positives that need a stopword filter rather than
renaming, genuine misses among worldbuilding proper nouns, and a third class whose
cause is not yet established. Training before the gate passes means fitting
in-copyright text with 86 identifiable source entities intact, in a corpus F02
already flagged as small enough for leak to be a real concern.

persistent-memory.md

@@ -206,6 +206,11 @@ _As of 2026-09-10 10:25 PT._
 
 ## Recent decisions
 
+- `[2026-09-11]` **BabyYarros D1 BUILT, D2 gender FIXED, D3 rename BLOCKED on the leak gate.** Operator: *"train the instruct on the yarros corpus -- babyyarros."* Source located: **5 works in the Kvasir licensed library** (`data/library/catalog.sqlite`, `rights=gated`) — Fourth Wing, Iron Flame, Wilder, Nova, Rebel. **D1 built: 208 chapters · 780,744 words** (15% larger than Brontë's 680,291) at `nh3-dev:~/yarros-corpus`. ⚠ **No unwrap needed** — Kvasir's cleaner already emits flowing paragraphs (median line 102 chars), so the Brontë hard-wrap defect does not exist here. **Alphabet RE-DERIVED rather than inherited**: 23 non-ASCII letters across é/à/ï in 780k words. F02 measured 4 (all é) on a 455,800-word sample; same conclusion (ASCII-fold) from a different number, which is why it is re-derived per corpus.
+- `[2026-09-11]` ⭐⭐ **NEW PATHOLOGY, worse than Brontë's: in a ROTATING first-person POV corpus, every book's narrator gets the WRONG gender.** Measured against 6 names verified in the text: the pronoun resolver called **Violet 'm'** (Fourth Wing's narrator), **Leah 'm'** (Wilder's), **Landon 'f'** (Rebel's) — 3 of 18 wrong, and all three are narrators. Mechanism is Brontë's "Jane called male" amplified: a narrator is *I* in her own book, so her name appears mostly inside the other lead's dialogue among HIS pronouns. ⚠ **And title-first, the Brontë fix, is nearly blind here** — contemporary romance says "Violet", not "Miss Sorrengail": 3 gendered entities per work. **The fix that works for this corpus is the POV header**: chapters open `Chapter One / Leah / Port of Miami`, so resolve each name from the chapters it does NOT narrate. Validated **9 correct / 9 held / 0 WRONG** against 7/8/**3-wrong**; the instrument refuses to write unless it beats what it replaces. `scripts/yarros-corpus/pov_gender.py`. ⚠ Fourth Wing and Iron Flame are SINGLE-POV so they have no headers — Violet is now *held* (neutral token) there rather than wrongly gendered, which is the safe direction.
+- `[2026-09-11]` ⚠ **Three real bugs found in `rename.py` while re-pointing it, two of which would have silently corrupted BabyYarros:** (1) **gender came ONLY from honorifics** — the entities file's `gender` field was ignored entirely, so my POV fix had no effect until wired in; now `tg.get(key) or e.get("gender")`, titles first so Brontë is unchanged. Effect: 1 → 13 gendered on `wilder`. (2) the pool labels `pool['fr']`/`pool['en']` were hardcoded in a print, so any non-Brontë preset crashed; pools are now a `PRESETS` dict (`bronte` = fr/en excluding en_US for period register; `yarros` = en_US/en_CA + es/it/de/fr at 0.62 US). (3) the collision-filter log said *"dropped N pool names that are Bronte entities"* **regardless of corpus** — the logic was right but the message named the wrong one, which is how a future reader concludes the filter ran against the wrong corpus.
+- `[2026-09-11]` ⛔ **D3 BLOCKED: leak gate at 86 of 232 renameable source entities surviving; Brontë's run reached 0 of 203.** Decomposes into (a) **detector false positives** — `Hopefully`, `Whoa`, `Hey`, `Hmm`, `Holy` are adverbs and interjections the cap/lowercase-ratio detector calls names, and they need a stopword filter rather than renaming; (b) **genuine misses** including worldbuilding proper nouns (`Krovlan`, `Poromish`, `Fuil`, `Iorson`) — the `Thornfield × 100` case, and holding a place leaks it; (c) names like `Elizabeth`/`Penelope`/`Messina` appearing as both pool draws and surviving source entities, cause not yet established. **Nothing has been trained.** ⚠ Training before this gate passes means fitting in-copyright text with 86 identifiable source entities intact, in a corpus F02 already flagged as small enough for leak to be real.
+
 - `[2026-09-11]` ⭐⭐ **THE INSTRUCT PROBE ANSWERS ITS QUESTION: voice and instruction-following DO coexist. Option C is de-risked.** `Qwen3-4B` **instruct** (not `-Base`), same corpus/seed/steps so the carrier is the only variable; best checkpoint `checkpoint-150` picked by loss (applying the 4B-Base lesson automatically this time). **Voice installed at full strength — curly quotes 16/18, IDENTICAL to the 4B-Base tuned arm's 16/18**, against the unadapted control's 1/18, and **task-leak 0/18 vs the base carrier's 4/18**. So the assistant prior did NOT block Brontë, which was the central risk. **Instruction-following SURVIVED: 10/10 on-beat through the chat template**, same as the untuned control. ⚠ **The cost is length discipline, not comprehension** — in-band 10/10 → **6/10**, median 124w → 140w. Training on Victorian prose made it wordier, a soft degradation rather than a break. ⚠ **Held-out 2.908 vs 4B-Base's 2.814** — the instruct carrier fits the corpus **0.094 nats worse** and **plateaus without turning** where base overfit at step 75: the assistant prior competes for capacity, so it absorbs less rather than overfitting more.
 - `[2026-09-11]` ⚠ **What raw-continuation training on an instruct carrier does NOT fix: the plot furniture.** Reading the product artifact, the tuned-instruct arm renders the beat and then drags the referent — *"He licked her clean… my master thus—my husband thus"*, turning the dog into a man, because Brontë's corpus is about masters and husbands. Another beat ran 247w and gave the narrator a list of duties. **This is exactly what instruction-PAIR training is for** — pairs teach "render this and stop", continuation teaches "keep writing Victorian prose". So the probe de-risks option C without substituting for it. ⚠ Also: my `ran_on` metric is uninformative on this job (10/10 on BOTH arms) because a single paragraph contains no blank line — it measures "no paragraph break found", which is correct and useless here. Do not read it as a finding.