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Commits
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 vh
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707fae2b2c |
perf(leak_gate): one alternation pass for the split scan — lv-hemingway went from timing out at 5 min to 35 s
Per-surface scanning is O(surfaces x copies x corpus). lv-mccarthy (108 surfaces, 36 copies) finished in 8 s; lv-hemingway (881 surfaces, 10 copies) was still running at 5 minutes and had to be killed. A gate too slow to run is not a gate. Same trick scan() already uses: build one alternation, map the matched string back to its surface by stripping separators. Regression: identical verdict and identical per-surface hit counts on the pre-fix lv-mccarthy tree (5 surfaces, 78 hits) and on the fixed one (0). Re-derived on the two shipped corpora with the committed instrument rather than a scratch probe: lv-hemingway GATE FAILED Pasionaria, Primitivo, Chicote -- 6 hits each, all 6 copies lv-bronte GATE PASSED 0 |
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c55966433f |
fix(lv-mccarthy): the leak gate passed with five protagonist names still in every copy
`leak_gate.py` scans `\b(Surface)\b`. Any character inserted inside a name defeats
that pattern outright, so a mangled occurrence is unrenameable by rename.py AND
unreportable by the gate. lv-mccarthy's 2026-09-17 tree passed at "0 of 75
renameable and 0 of 37 sub-threshold" while carrying 13 occurrences of Bell,
Chigurh, Moss, Toadvine and Glanton in all six copies:
B ell C higurh M oss T oadvine a small-caps drop cap kept as its own token
Toad-vine Glan-ton a print line-break hyphen kept by the extractor
Every visible occurrence HAD been renamed, which is what made the residue invisible
to a spot-read. Fixed at three levels, all three of which must stay:
build_corpus_mccarthy.py rules 4 and 5 repair the source text — 32 split initials
with a lowercase remainder, 5 hyphen-split names, each with an expected count so a
master change fails the build. Rule 4's letter class is consonants only: `I` opens
1,966 paragraphs, `A` 143 and `Y` 32 (Spanish `y`); folding any would corrupt 2,141
lines to fix 32.
leak_gate.py gains a separator-tolerant pass with its own positive and negative
controls, and it FAILS the gate. Validated against the pre-fix tree: reports all
five surfaces, exits 1. Its fragment filter is what makes it usable — a naive scan
returns 18 false positives on Hemingway (`God damn`, `I run`) against 3 real ones;
requiring one fragment to be a non-word of the corpus cleared all 18 and kept all 3.
The whole D1→D3 chain is reproduced byte-identically before and after, so the fix
is the only delta: 6 works, the entity map, the final map and all 36 copy files.
Cross-checked on the shipped corpora: lv-bronte is clean of this class, lv-hemingway
carries 3 (`Primi tivo`, `Pasionar ia`, `Chi cote`) and is live on fv-ml1.
Also in build_sft_pairs.py, both needed before lv-mccarthy's pairs:
DEFECT 4, hard-wrap reflow. Measured on the SHIPPED lv-bronte adapter, which emits
mid-sentence line breaks at 12.46 per 1k chars against 0.00 for its own base control
and 0.00 for every Hemingway arm. McCarthy is the mixed case — The Road is wrapped,
the other five works are not — so the corpus teaches the break as a coin flip. The
obvious fix (join every interior newline) corrupts 46 two-speaker exchanges whose
blank line was lost, and unmarked dialogue is the one thing this adapter exists to
learn; the rule splits on sentence-final punctuation instead and takes the cheaper
error. Self-targeting and off by default, so every shipped pair set is unchanged.
A `mccarthy` register, which names the punctuation deliberately: the eval drives the
base control arm with this same prompt, so tics left out of it are a surface trick
only the adapter can perform, and delta_cb is a character-bigram measure.
drop_leading_heading now also consumes Blood Meridian's dash-separated chapter
arguments — 131 paragraphs, 0 in every other work of all three corpora.
And a RUNBOOK, because the D1→D3 session recorded nothing and the chain had to be
recovered by rebuilding candidates and matching sha256 against the artifacts on disk.
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5ddb0472e4 |
lv-mccarthy D3 on gx10: leak gate PASSED, and the val split is now bigger than Hemingway's
~/lv-mccarthy on pfi-gx10: corpus-clean, corpus-renamed (6 copies, 1,002 records), scripts.
leak gate 0 of 75 renameable and 0 of 37 sub-threshold survive in any copy
positive control 108/108 surfaces found in the unrenamed source
negative control nonce absent from both trees
THREE McCARTHY-SPECIFIC DECISIONS, each forced by a measurement.
1. --scope corpus, NOT the default per-work map. The Border Trilogy shares characters
across books -- 9 surfaces appear in more than one work, including Parham (The
Crossing + Cities of the Plain), Grady and Cole (All the Pretty Horses + Cities of
the Plain), Socorro and Héctor. A per-work map would give John Grady a different
invented name in each novel, turning one character into two.
2. A NEW `mccarthy` rename preset rather than reusing `hemingway`. Both are
Spanish-inflected, but Hemingway's romance pool carries it_IT and fr_FR for his
Italian and French casts, and McCarthy writes neither language -- drawing from it
would drop Italian and French surnames into a Texas-Mexico border novel. en_GB goes
for the same reason. en_US + es_MX/es_ES at an even share.
3. --min-cap 5 to MATCH the entity map's floor. The first gate run FAILED with 45
survivors, and the diagnosis is the Brontë lesson exactly: entities.py admits
cap >= 5 while rename.py only renamed cap >= 8, so every entity between 5 and 7 sat
in the map, was never renamed, and was counted as a leak. Hemingway never hit it
because its map had sub_threshold_total 0.
⭐ --holdout-chapter NOW TAKES A LIST, and this is the change with the most downstream
effect. The val split is one chapter index per work, so its SIZE is set by how many
WORKS a corpus has, not how many words:
Hemingway 10 works -> 9 val units -> 36,563 words/copy -> gate DECISIVE
Brontë 4 works -> 4 val units -> 17,043 words/copy -> gate MARGINAL
McCarthy 6 works -> 6 val units -> ~18,000 would have been Brontë's end of that
Holding out chapters 7 AND 17 gives 11 units and 40,653 words per copy -- larger than
Hemingway's, at a cost of 7% of the corpus -- on a corpus 40% smaller than his. No
amount of corpus size fixes a val split that scales with work count.
THE HUMAN GENDER PASS IS NOW AN AUDITABLE FILE, not a hand edit. The honorific/window
resolver scored 21 correct / 3 held / 1 WRONG against a 26-name control; the base-rate
proximity resolver built for Hemingway scored 18/6/1 and its own guard correctly
REFUSED to write. So the incumbent stands and four entries are fixed by hand in
gender_overrides_mccarthy.json, each carrying its evidence.
⚠ All four are female and all four look male-dominated in raw pronoun counts, because
this corpus runs 29,144 male pronouns to 5,036 female -- a base rate of 85.3% male.
Carla Jean Moss at 31m/21f would be 44m/8f at that base rate, so 21 female against an
expected 8 is decisive. Same arithmetic that recovered Pilar and Brett on Hemingway.
Alfonsa was in my control set and is correctly absent from the map at 4 occurrences,
below the min-count floor -- an error in the control, not the pipeline.
apply_gender_overrides.py refuses two ways: a name absent from the map is an error
rather than a silent no-op, and overruling a gender the detector already holds needs
an explicit "correcting": true so it cannot look like filling a held entity in a diff.
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5aa10bf138 |
lv-mccarthy D2: entity map + stoplist, both audits green — and audit_stoplist was scanning its own rationale
Entity map at ~/mccarthy-corpus/entities.json. 123 surfaces after a 107-surface stoplist.
entities.py 27/27 controls -- 19 positive (Glanton, Toadvine, Rawlins, Blevins,
Alejandra, Chigurh, Moss, Bell, Boyd, Holden, Tobin, Magdalena,
Eduardo, Parham, Socorro, Webster, Redbo, Niño, Franklin) and 8 negative
audit_stoplist PASS -- no stoplisted surface is ever addressed as a person
audit_entity_map PASS -- positive `boy` 0.89, negative band tops out at Riddle 0.17,
all 5 remaining flags on the read-and-cleared list
⚠⚠ A DEFECT IN audit_stoplist.py ITSELF, latent for every corpus before this one. It built
its surface set from every list value in the stoplist JSON -- including `_why`, which by
convention is a LIST OF PROSE LINES. Every sentence of the rationale went into the matcher,
and the empty separator line matched the honorific pattern 139 times, printing a flag with no
surface name at the top of the report, above the one real catch. It now skips `_`-prefixed
metadata keys and empty strings.
THE ONE REAL CATCH WAS A CONTRADICTION INSIDE MY OWN FILE. `Franklin` sat in the geography
list because it is the old name for El Paso, while the same file's context note recorded
'I'm here to see Mr Franklin' -- a lawyer in All the Pretty Horses. The honorific audit found
the contradiction between the two halves of the file. Franklin is now renameable.
A SECOND SELF-INFLICTED ONE: the fragments list was a speculative A-Z, which stoplisted `I`
and `A` -- ordinary English words -- and `Sir I dont think I can do that` duly tripped the
honorific audit. It is now the four letters actually MEASURED as entities (E, H, T, K).
Stoplist what the entity map produced, not the alphabet.
Everything ambiguous was read in context before placement, and the reasoning is in the file:
Socorro is the ranch COOK in Cities of the Plain, not the New Mexico town -- renameable
Webster, Jackson, Harlan, Lamar are Glanton's men and lawmen, not places -- renameable
Niño, Keno, Redbo are HORSES, the author's inventions -- renameable, the `Inglés` precedent
Mangas, Travis, Venada, Moderno are genuinely dual-use -- renamed, the safe direction
Santa, Varas, Griffin, Eagle, Avenue, Calle, Terrell are real geography -- stoplisted
Yaqui and Gilenos are real peoples; Ford and Hashknives are a brand and a real outfit
Ed (Ed Tom Bell) and JC are short but are names, read and kept renameable
Sensitivity floor, stated because it is part of the result: the top 170 of 199 surfaces were
classified. The bottom 29 were not individually read, so a rare real-world referent may be
renamed -- the safe direction, an accepted cost, not an oversight.
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705fa3a65b |
split_units: choose a unit mode by SIZE, not by count, and fall back to paragraph blocks
McCarthy and Krakauer both need this before a corpus can be built, so it is a shared module
rather than a third copy of the Hemingway splitter.
THE INHERITED RULE IS "MOST UNITS ABOVE A FLOOR" AND IT BREAKS ON PART MARKERS. Measured:
Cities of the Plain 4 roman marks -> 4 units, median 22,312w <- the book's PARTS
The Crossing 4 roman marks -> 4 units, median 37,310w <- same
"Most units" scores 4 over the 1 that finding-nothing gives, so it wins, and the existing
guard only fires at exactly one unit. A 37,000-word "chapter" sails through and every
downstream tool accepts it. Size is now the eligibility test: a mode qualifies only if its
median unit is inside [600, 12000] AND no single unit holds half the work.
TWO THINGS A CONTROL RUN CAUGHT, BOTH NOW FIXED IN THE RULE. The first version scored
eligible modes by "median closest to target". Run over Hemingway, whose markers are known
good, it chose caps-title over the book's own chapters on True at First Light:
bare-numeral 20 units median 5,337w max 11,155 <- the real chapters
caps-title 6 units median 777w max 113,886 <- median looked BETTER
caps-title matched five stray all-caps lines, so five tiny units sat beside one holding 97%
of the book. A median cannot see that distribution; a max bound can. And caps-title is the
weakest of the four signals, which is why the tiebreak among eligible modes is now PRIORITY
(contents > chapter-word > roman > bare-numeral > caps-title), not size.
CONTROLS, both green after the fix:
positive Hemingway's ten works, markers known good -> 8/10 reproduce the shipped mode and
unit count exactly. The two differences are explained, neither is a mode error:
short-stories used `contents`, which the harness does not supply, and The Old Man
and the Sea was deliberately kept whole as CONTINUOUS.
negative 40,000 words with no blank lines -> 1 unit. It refuses to fabricate divisions
out of unstructured text rather than returning a plausible section count.
Result on the two new authors: McCarthy 167 units / 587,233 words, Krakauer 135 / 431,938,
both median ~3,200-3,500w against Hemingway's 3,128.
⚠ CORRECTION TO AN EARLIER SURVEY. I reported that all four Krakauer works carry zero
chapter markers. That was wrong and it was my regex, not the books: the survey pattern
required "Chapter" followed by a numeral, and Krakauer writes "CHAPTER ONE". Missoula and
Where Men Win Glory split on chapter-word (33 and 41 units); Into the Wild and Under the
Banner of Heaven on caps-title (28 and 33). Only McCarthy's All the Pretty Horses, Cities of
the Plain, The Crossing and The Road actually need the fallback.
The Hemingway builder is deliberately NOT repointed at this module. Its corpus is shipped and
its provenance sha is pinned by a live adapter; the one behavioural difference (The Old Man
and the Sea would section into 9 rather than stay whole) is an improvement nobody asked for
on a corpus nobody should churn.
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9f35c8d659 |
booth: four arms, one beat, one author-neutral prompt
Six beats through voices-base, lv-bronte, lv-yarros and lv-hemingway, all served from the same process on fv-ml1 :8027 so only the adapter varies. Operator-requested side-by-side. http://10.100.10.50:8090/b/lv-voices-four-arms/ (24h TTL; also on the link board) THE PROMPT NAMES NO AUTHOR, deliberately. Each adapter trained under a prompt naming its own, so driving all four with any one of those hands that arm a hint the others do not get and the page would be measuring the prompt rather than the voice. The shared task skeleton is kept and the author clause removed. One asymmetry is disclosed on the page: Brontë and Hemingway trained on "a SHORT PASSAGE ... may run to several paragraphs" while Yarros trained on "ONE paragraph", so the neutral prompt sits slightly off-distribution for all three rather than for one. THE CONTROL GETS A 4x LARGER TOKEN BUDGET, and publishing it any other way would have been dishonest. Measured at the gate's 320-token budget: voices-base median 26 prose words, 181-257 words of <think> planning first, and 5 of 12 cells never reach the prose at all the adapters 0 of 12 failures each, empty think block in 12 of 12, median 97-105 words The adapters learned to skip the reasoning phase; the carrier has not. Showing the starved control would conflate voice with budget discipline, so the control runs at 1200 tokens and finishes every time, median 121 words. Both numbers are on the page. Two seeds per cell behind a toggle, because one sample of a sampled process is an anecdote, and a blind-mode toggle that hides which column is which. Sampler matches the gate harness (temperature 0.9, top_p 0.95, "BEAT: " prefix). Checked before publishing rather than after: all 36 adapter generations scored for verbatim 8-gram reuse, each arm against ITS OWN corpus. Brontë 0, Yarros 0, Hemingway 2 of 12 with a longest run of 8 words, that run being "i don t know i don t know". Layout verified by rendering it, not by reading the CSS: four equal 374px columns at 1600px wide, no horizontal overflow, 24 cards, 48 panes. ⚠ nh3-dev's shared /opt/ms-playwright tops out at chromium-1234, so playwright must be pinned to 1.61.0; a bare `npm i playwright` pulls 1.63 and asks for a browser build that is not there. |
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5e6611466c |
audit_pairs_sourcenames: --filter-out, so the detector is also the fix
An already-built pair set cannot be repaired by build_sft_pairs.py --source-entities;
that flag only works at generation time. Hemingway's and Yarros's sets both predate it.
The contamination is in the BEAT, so dropping the row removes it outright. Measured on
the Hemingway train pairs: 7,094 -> 7,024, 70 dropped, 0.99% of the training data. That
is cheaper and cleaner than regenerating 70 beats against a second generator session,
which would leave the set mixed-provenance for the sake of 1% more data.
Verified by read-back rather than by the write succeeding: re-auditing the filtered file
reports 0 of 7,024 on both columns, controls green, GATE PASS.
Two refusals rather than a best-effort write:
- a contaminated RESPONSE column aborts. That is a different fault -- pairs built
against an unrenamed corpus -- and dropping rows would hide it instead of fixing it.
- more than one --pairs input aborts, because the output is a single file and would
silently merge train and val into one.
Also cross-validated the detector against the lv-bronte pair sets on real data, where the
answer is already on the record:
pairs-full + pairs-val (post-fix) 0 of 3,858 matches the recorded "0 leaks across
3,858 pairs" exactly
pairs-full.CONTAMINATED 15 of 792 = 1.89%, Rochester x6, Jane, Brocklehurst
x2, Beck, Fairfax, Burns, Helen, Eyre -- against a
record of "13 of the first 714 beats (1.8%)" with
the same names
An independently written instrument reproducing a documented finding at the right
magnitude, on the right names, is the control that says its zeroes mean absent and not
blind.
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051b99e063 |
audit_entity_map: the rename can damage the prose and no gate will ever say so
audit_stoplist.py finds surfaces wrongly held OUT of the entity map -- a stoplisted character is an undetectable leak. This is the mirror: surfaces wrongly held IN it. leak_gate.py only ever asks whether the author's names are GONE, never whether non-names were spared, so renaming `the Chinese` into an invented surname passes it perfectly. Found sideways on Hemingway. The pairs audit reported beats naming African, Chinese, X-ray, Republican and Cezanne as leaks -- correctly, those surfaces really were removed from the corpus. Reading why turned up the larger defect: they should never have been renameable in the first place. Measured on the Hemingway map, both controls green: positive `other` 764/1356 article-preceded = 0.56 negative 100 honorific-confirmed people, highest Inglés at 0.26, bulk 0.00-0.06 FLAGGED 130 of 946 surfaces, 1,616 instances = 0.162% of corpus words The signal is an article in front of the surface: you write `the Frenchman` and `a Martini`, never `the Rinaldi`. It is a heuristic and every hit is reported FOR READING, never auto-removed -- `the Widow` and `the Informer` are genuine Hemingway epithet-names that SHOULD be renamed, and the band's own top entry makes the point, since Inglés at 0.26 is an in-world nickname deliberately kept renameable and sits just under the bar. Initials are excluded from the negative-control band rather than admitted to it. `Mr. P.` is an initial, not a person, so letting it in lets a map defect poison the control that validates the detector -- on Hemingway `P` (0.32, every occurrence `the P. O. U. M.`) was the one surface failing a band whose next highest was 0.26. Initials take no article and are invisible to the scan anyway, so every surface of two characters or fewer is now listed unconditionally. Sixteen of them are in this map, C at 274 occurrences; the same class as the `G` that was caught by hand about to be renamed to a surname 248 times. The unresolved count that drives the exit code is computed over every flagged surface, not the --show slice. Tying a gate's verdict to a display flag is the same defect as a log filter that turns a real event into a clean zero. Also corrects a wrong claim in audit_pairs_sourcenames.py's docstring: the Hemingway rename did not HOLD 591 surfaces. Paris, Madrid and Spain survive because the stoplist keeps them out of the entity map before it is built, so the map is exactly the removed set -- 941 surfaces, 941 removed, 0 kept. Measured per run rather than assumed, because a pipeline that carried kept surfaces into the map would report every `Paris` as a leak. |
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0bb4938518 |
lv-hemingway: pre-register the v2 gate, and fix the floor rule that decided lv-bronte
The gate design is written before any generation exists, because lv-bronte's
verdict turned on a choice that was only visible after the numbers printed.
THE FLOOR RULE IS NOW PAIRWISE. lv-bronte computed the noise floor as the largest
within-arm seed spread across ALL arms present. Its ckpt475 shipped at +0.193
against a 0.251 floor set entirely by ckpt925 -- a third arm nobody was shipping,
on one outlier seed. Scored against the arm it was actually compared to, the floor
is 0.092 and the same gap clears at 2.1x. A candidate's verdict must not depend on
which other arms happened to be generated. voice_distance.py now prints both floors
and flags any disagreement, so the lv-bronte record stays comparable.
audit_pairs_sourcenames.py closes the blind spot leak_gate.py has by construction:
it reads the corpus and the renamed copies, never the generated beats, so it cannot
see a beat-writing model restoring the author's real character names. Run over the
Hemingway pairs, which predate build_sft_pairs.py --source-entities:
val 0 of 200 -- the eval fixture is clean, the gate is unconfounded
train 70 of 7,094 (0.96%) -- Santiago x16, Catherine x7, Rinaldi x3, Brett,
Harry, Jake, Pablo, Nick, Maria ...
responses 0 of 7,294 -- the lv-bronte beat-only signature exactly
A matched surface is only counted when the rename actually removed it, verified
against the renamed copies, so a beat naming a held real-world place is not a leak.
Controls run every time: 941/941 surfaces found in the unrenamed source, nonce
absent from both trees, and 6 planted canonical names detected 6/6.
voice_distance.py --author is now REQUIRED. It was hardcoded "Yarros" and printed
"reference: held-out Yarros" over Brontë's numbers into a committed artifact. A
default would have moved the silent-wrong-label failure rather than removed it. The
stale "one seed-pair per arm / corroborates Base < Instruct" footer is replaced with
what the run actually carries.
Gate design: three arms (base-unadapted, ckpt1750, ckpt850), 60 beats, 4 seeds.
ckpt850 is present because the loss curve cannot separate it from ckpt1750 -- +0.0040
against a 0.0044 median neighbour jitter, with three checkpoints inside one jitter of
the minimum. adapter/ is excluded: +0.0762 is 17.4x the jitter and is resolved without
a gate.
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8bb7686a16 |
audit_stoplist: a stoplist entry is an assertion the leak gate cannot check
Stoplisting a surface removes it from the entity map, so rename never touches it
and the gate never scans for it. That is exactly what a stoplist is FOR when the
surface is a real-world referent — and exactly how a wrongly stoplisted CHARACTER
becomes an undetectable leak. The gate reports 0 of N surviving and is telling the
truth about the set it was given.
Found by luck on lv-bronte: a generated beat said "Mrs. Leaven", and Leaven had
been filed under scripture as the bread noun. Reading it back: "Robert Leaven,
the coachman" — Bessie's married surname in Jane Eyre.
Running the audit instead of trusting that luck caught two more:
Pierrot "Madame Pierrot: she comes from Lisle, in France" — a teacher in
The Professor, filed as the commedia dell'arte figure
Samuel "Mr. Samuel Wynne" — filed as scripture
and correctly CLEARED two:
Wellington "that Baal of a Lord Wellington" — the real Duke
Moses "the Rev. Moses Barraclough" — the documented dual-use
Signal is an honorific in front of the surface: real-world referents are not
addressed as Mr/Mrs/Miss/Madame/Lord. It is a heuristic and not a proof, which is
why every hit is REPORTED FOR READING and never auto-removed — Wellington and
Moses both trip it and both are correct. Exit 1 on anything not on --allow, so it
can gate a pipeline.
Blast radius of the three errors was 16 of 3781 train pairs and 3 of 80 val —
small, but they are the author's characters in training data, which is the one
thing this pipeline exists to prevent. Corpus rebuilt rather than dropping the
affected pairs: a corpus on disk that disagrees with its committed config is how
superseded claims get made. Gate re-passes at 0 of 368 (three more surfaces than
before, exactly the restored characters), both controls green.
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e9e8c40b83 |
eval harness: sample the beat fixture from held-out val, and bind the eval prompt to the trained one
Two harness defects that would each make a voice number uninterpretable. build_beat_fixture.py — the fixture is now SAMPLED from the val split rather than hand-written. The original BabyYarros fixture was five hand-written beats about a stray dog and a kitten: wrong genre, so 'He licked her clean' came back as explicit sex from a romantasy adapter, and n=5 had a noise floor of 0.800 that manufactured a +0.45 result which collapsed to +0.08 at n=120. Sampling from val makes it in-genre and held out by construction, spread across works so a naive head(30) is not one novel. Refuses outright if the pairs carry any split but val, because a fixture drawn from training data makes every downstream number a memorisation measurement wearing a voice label. gen_beats_chat_yarros.py --system-from — the SYS constant in this harness is Yarros's. Driving a Bronte or Hemingway adapter with it measures the arm under a system prompt it was never trained on and confounds the carrier change with a prompt change. Rather than duplicate the register table and rely on whoever runs it to pick the matching one, read the prompt out of the pair build's own provenance, which is the artefact that records what the adapter actually saw. |
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 Vuong Hoang
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03b4a3f62c | BabyHemingway D2+D3: entities, base-rate gender resolver, rename preset, leak gate passes | ||
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Vuong Hoang
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efb734586b | BabyYarros: raw-surface scoring and a memorization check with both controls | ||
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Vuong Hoang
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713e83dd5e | BabyYarros: pre-register the pair-vs-rawtext decision rule before the arms are read | ||
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5558d9c7d3 |
R49/BabyYarros: voice + beat evaluation tooling and the seat-free delta_cb
Adds the Yarros-side evaluation the training exists to justify: does the adapter move arbitrary prose toward Yarros, and can the instruct arm still expand a beat to a paragraph on direction. Yarros-flavoured voice prompts (modern/neutral/ romantasy tiers so any Yarros voice in the modern tier is adapter-attributable, not prompt-supplied) and a Yarros-register beat SYS on the chat generator. voice_distance.py is the honest slice of adjudication that needs no seat: Burrows's Delta over character bigrams against held-out Yarros. Its first cut mis-framed the noise floor — it used the same-author distance (held-out vs itself) as the between-arm significance threshold, which is the target, not the threshold. Fixed to the measured floor: the within-arm seed spread, which is this metric's sampling variance at this sample size, computed from the two seeds already generated rather than assumed. Result on the built corpus, ordering base-125-tuned < instruct-tuned < base-unadapted, both adapters clearing the 0.046 measured floor (base +0.157, instruct +0.076), and the ordering corroborating the independent held-out loss ordering (Base below Instruct). One seed-pair per arm, so it corroborates rather than settles; the full frozen adjudication still needs a romantasy control panel, a second seed, and the gen seat for the beat-incumbent leg. |
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7b0580dcbe |
BabyYarros: the leak gate passes, and it found three defects nobody was looking for
The gate is new. There was no committed instrument for "does any of the author's own proper nouns survive the rename" -- the Brontë number was produced by hand -- so leak_gate.py is now that instrument, and it runs both directions every time: the same scan over the unrenamed source as a positive control, and a nonce string as a negative one. A detector that only ever sees renamed text cannot distinguish absent from blind. Run against BabyYarros as built it reported 212 surviving entities, not the 86 recorded earlier, because it scans the whole corpus rather than each work separately and it counts the sub-threshold entities rename never looked at. Three findings came out of closing that. The corpus had a typography defect of its own. The D1 notes correctly say no unwrap was needed; a different defect was there instead. The Empyrean books set their chapter epigraphs in small caps and the extractor rendered the run as uppercase while leaving the large initial as a separate token, so the corpus carried "M AJOR A FENDRA'S G UIDE TO THE R IDERS Q UADRANT" -- 106 lines, ~700 splits -- plus 52 drop caps like "T he flight field". That is where the entities called IDERS, UADRANT, NAUTHORIZED and seventeen bare single letters came from. A split initial next to an uppercased run is enough to recover the original mixed case, so the restore is exact rather than approximate: a word with a split initial was capitalised, an all-caps word without one was lowercase. Back matter was inside the prose. The builder splits on chapter headings and nothing follows the last one, so every work carried its acknowledgments, newsletter pitches and cover-artist credits -- 4,555 words naming the author's agent, editors and children, in a corpus whose entire purpose is that no identifiable name survives. And the gate passed at 0 of 314 while Afendra was still in every copy. The name never appears unpossessed, so it keyed as an apostrophe form, and rename and the gate both skip those as contractions -- unrenamed and unreported at once, which is the worst failure shape available. Baxter escaped a different way: wilder renders an in-book news article entirely in lowercase, putting the cap/lowercase ratio at 0.13 against a 0.05 bar. Then a second class the unigram scan structurally cannot see. Riders Quadrant, Flame Section, War Games and Fourth Wing -- the book's own title -- are built from ordinary words the detector correctly refuses to call names. The gate now audits recurring capitalised 2-3grams against an explicit allow list, and rename applies a phrase map after the entity pass. Every new detector flag is opt-in and off by default, and the Brontë entity map was re-derived after each change and confirmed identical in keys, surfaces and every field. The stoplist was built by reading each surface in context, which is why it is short: Violence is Xaden's nickname for Violet, and Continent, Presentation, Barrens, Originals, Montserrat, Athena, Aura, Curator and Sage are all in-world. A plausible-looking guess would have excluded most of them. Final: 0 of 325 entities and 0 of 91 audited phrases survive in any of 30 copy files, both controls passing. The sensitivity floor is stated in the gate's own output -- 3 occurrences for a name, 5 for a phrase -- because a negative without one is unfalsifiable. |
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6dba912324 |
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. |
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e15c5ee5ea |
Instruct probe: voice and instruction-following coexist
Trained the same corpus onto Qwen3-4B instruct rather than -Base, with seed, steps and token count held so the carrier is the only variable. The chain picked checkpoint-150 by loss automatically, applying the lesson that cost a re-cut on 4B-Base. The central risk did not materialise. The assistant prior did not block the voice: curly quotes land at 16 of 18, identical to the 4B-Base tuned arm, against 1 of 18 on the unadapted control, and task-leak is 0 of 18 where the base carrier leaked 4. Instruction-following also survived raw-text training -- 10 of 10 on-beat through the chat template, the same as the untuned control. The cost is length discipline rather than comprehension. In-band dropped from 10 of 10 to 6 of 10 and the median went from 124 to 140 words. Training on Victorian prose made it wordier, which is a soft degradation and not a break. Held-out sits at 2.908 against 4B-Base's 2.814, and it plateaus without turning where the base carrier overfit at step 75. The assistant prior competes for capacity, so the instruct carrier absorbs less rather than overfitting more. What raw-continuation training does not fix is the plot furniture. 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 the corpus is about masters and husbands. Another beat ran to 247 words and gave the narrator a list of duties. That is precisely what instruction-pair training addresses, since pairs teach render-this-and-stop where continuation teaches keep-writing. The probe de-risks the instruction-pair path without substituting for it. One metric note against future misreading: ran_on reports 10 of 10 on both arms and is uninformative on this job, because a single paragraph contains no blank line for it to find. |
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d9cf486004 |
Settle the Skaldsong architecture question with a ten-format negative result
Skaldsong's requirement is beat-sentence in, paragraph out, stitchable. That is narrower than free-form continuation and it fails differently, so it was tested directly rather than assumed from the earlier prompt work. The adapted 4B carrier cannot do it. Ten prompt formats across three seeds each -- bare, paragraph-break, labelled, epigraph, one-shot, one-shot unlabelled, three-shot, "it happened in this way", "I remember the whole of it", and the letter prompt's label-and-begin move applied to a beat -- produced thirty samples and not one that reliably renders the beat. They drift, they frame, or they truncate. The cause is structural rather than a shortfall of the adapter: "write a paragraph about this sentence" is an instruction, and a completion model has no mechanism for "about". It continues the text it is handed. Two formats failed worse than drifting and leaked pretraining task data. The paragraph-break format emitted an NLI multiple-choice item, and label-begin emitted a grammar-correction exercise. A standalone sentence followed by a blank line looks exactly like a dataset entry, and style adaptation does not remove base-model task artifacts. The same five beats through the gen seat with a style instruction and no adapter landed all ten samples inside the requested 90 to 140 word band, every one on its beat, with no drift into a following scene. The voice, however, is generic literary pastiche -- abstract-noun-heavy, fond of aphoristic openers, and over-written -- rather than Brontë. Voice without direction against direction without voice. The product needs both, which makes rebuilding the corpus as instruction-response pairs on an instruct carrier the evidence-backed option rather than more prompt cleverness. This applies to Yarros identically, because the carrier question is orthogonal to the author, so the next corpus should not re-run this experiment. Stitching also has its own failure mode, shown in the booth's third panel: the paragraphs were generated independently and the point of view slides between them, so by the fourth beat the narrator is both watching the girl carry the animals and carrying them herself. A real stitcher has to feed prior paragraphs back as context, which means the instruction-pair corpus wants multi-paragraph continuity examples and not only isolated beat-to-paragraph pairs. |
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8787daf04f |
Find the prompt recipe that works on a completion carrier
Two operator prompts settled the question the handoff prompt opened. The winner labels the artifact and then begins it: "This is the letter I wrote verbatim, my two short paragraphs, detailing the time I saw the mangy gray dog meet and then lovingly and tenderly lick a calico kitten: Auntie, You'll never believe what I saw--". Two of three seeds delivered the actual event in first person, and one is the strongest output of the sweep -- the dog following her to the mill, the kitten caught in a hedge, the dog carrying it out and laying it in her lap before licking it. Coherent arc, correct subject, no gloom-override, no meta-frame. It also reproduced the Gutenberg underscore-italics convention. It works where the handoff failed for a specific reason. The handoff could be satisfied by narrating compliance, because the letter did not yet exist. Naming the artifact and starting it leaves nothing to narrate around, so the only continuation is the artifact. The chapter-heading prompt refuted a hypothesis of mine. I predicted that rendering the title in the corpus's own conventions would make it land harder than the operator's inline form. It did the opposite: both corpus-form seeds ignored the title and opened unrelated scenes, while the inline form at least finished the heading and wrote a chapter about the story. Corpus chapter titles are short and decorative, so a long descriptive one in that slot reads as decoration to skip. A label only instructs if the model treats that slot as load-bearing. That prompt also surfaced an unnoticed consequence of the D2/D3 rename pipeline: given "Alice", the adapter produced "Alexander the Alexander, as he was known in Little London". The corpus was entity-renamed from a French/English pool, so the adapter learned that character names come from that pool and rewrites outside names into it. Callers cannot reliably name their own characters at prompt time. That is not a defect of the rename, which exists to prevent memorising Brontë's cast, but it is a usability constraint worth stating. |
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e47295d2ce |
Re-cut the 4B arms from checkpoint-75 and test an in-fiction instruction
The 4B tuned arms now come from checkpoint-75, the actual loss minimum at 2.813826 confirmed from loss-series.json rather than from reading the training log. Only the tuned arms needed re-cutting; the base arm never touches the adapter. One surprise in the comparison: step-75 and end-of-run differ on typography rather than voice. Curly quotes read 16 of 18 against 17, collapse is 0 either way, but the hard-wrap ratio is 0.33 at step-75 against 0.12 at the end. Further training washes the residual line-break habit out while held-out loss gets worse, so best loss and best typography are different checkpoints. Neither is near the original 0.85 defect and the corpus's own residual from preserved verse is 0.25. The operator's second prompt embeds the instruction inside the fiction: Abernathy tells the tale badly and then asks the narrator to retell it in a few short paragraphs. Across six seeds the model acknowledged the handoff every time and never once performed it. It wrote "I told it, briefly, to his satisfaction", and "So I wrote it out, and kept it in my pocket-book", and one seed negotiated the brief in character, objecting that it could not condense the story into a few short paragraphs because there were too many points to touch. The reason is structural rather than a failure of the adapter. In a novel, "she retold the story" is an ordinary sentence, so the likeliest continuation of a request is narration of compliance rather than compliance. A trailing paragraph break did shift the behaviour -- one seed opened in the narrator's own quoted speech -- so typography is a real lever, just not a sufficient one. That is direct evidence on the instruct question raised earlier: if the product is asking for a scene and getting the scene, no amount of in-fiction framing substitutes for a post-trained instruction-follower, which favours rebuilding the corpus as instruction pairs over more prompt cleverness. |
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25eb65e23b |
Complete the R49 carrier sweep: 4B closes the continuity gap and overfits
The three rungs now sit on the same unwrapped corpus, seed, step count and token count, so carrier size is the only variable. Held-out loss reads 3.329 at 0.6B, 3.018 at 1.7B and 2.814 at 4B -- deltas of 0.311 and then 0.204, diminishing but still real. 4B answers the question the rung existed for. Scene-level continuity holds: on the office-kitchen prompt it produces a named character with motivated dialogue, a spatial layout the narrator navigates, and a physical description, all in one passage, where 1.7B wrote pretty but eventless prose about opening doors and looking at stars. On the letter prompt it opens the letter, promises to quote it, and then quotes it across a paragraph break. Voice saturation is also the best of any rung: curly quotes 17 of 18 against its own base arm's 1 of 18, and collapse 0 of 18 against 4 of 18. Two findings that change earlier conclusions. 4B is the first rung to overfit inside one epoch. Its series runs 2.832, 2.816, 2.814, 2.820, 2.824, 2.825, 2.825 -- a minimum around step 75 and then a turn. Both smaller rungs plateaued without turning, so the optimal epoch count shrinks as the carrier grows and my earlier "one epoch is right for this corpus" holds only for the small end. The consequence is operational: the adapter directory holds the end-of-run weights at 2.825 rather than the step-75 best at 2.814, and it exists as a recoverable checkpoint only because save_steps was set. The voice arms were cut from the end-of-run adapter, so the booth understates 4B slightly. The tone-override also appears to close. On the operator's frame prompt asking for a wonderful story, 1.7B held the frame on every seed but killed the animals on two of four; 4B kept them alive on both seeds, and one of them had the narrator doubt the story he was told and supply a parallel childhood memory to explain the doubt. That is a narrator with an interior position on the tale. Two samples per arm, so directionally right rather than established. |
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4cee4b179d |
Launch the 4B rung and record that voice transfer carries subject matter
Rung 3 of the sweep is training: Qwen3-4B-Base, one epoch, seed 4919, the same unwrapped corpus, 159 steps at roughly 37.8 s/it for about 100 minutes. Carrier size remains the only variable across the three rungs. A two-arm voice test is chained behind it, gated on the adapter existing, so the rung is readable when it lands rather than needing a second round trip to become informative. The finding worth keeping came from an operator prompt rather than from my test set. Asked for a wonderful story about a stray dog licking a calico kitten, framed as reported speech, the 1.7B adapter held the frame across all four seeds and was unmistakably Victorian throughout -- and two of the four turned the premise into animal death, with the cat licking the puppy to death and the narrator's interlocutor answering that he wished they were all dead. That is not a coherence failure. It is Charlotte Brontë's preoccupations arriving alongside her sentences: Jane Eyre opens on a beaten child, Helen Burns dies, and Villette is saturated with grief. Train 680k words of that into a small carrier and a request for something wholesome reaches for mortality. My nine test prompts were all emotionally neutral and structurally could not have surfaced this. The operator's prompt found it on the first try, which is worth recording as a property of the test set rather than of the adapter. The implication for the regime is that "voice transfer" includes tone and subject, so wanting the voice without the gloom is a corpus-selection or prompt-framing problem rather than a training-length one. Also observed: one seed closed its anecdote and began "CHAPTER XIX. THE CHILD'S CLOSET", having learned book structure unprompted. The base control on the same prompt went modern and essayistic, so the shift is the adapter rather than the carrier. |
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9e18fd58ec |
Complete R49 rung 2 and booth the three-way voice comparison
Both rungs now sit on the same unwrapped corpus with seed, steps and token count held, so carrier size is the only difference and the effect is attributable: held-out 3.329 at 0.6B against 3.018 at 1.7B, a gap of 0.311 nats. The chained 0.6B rerun closed the confound the unwrap opened. Two things in those numbers need stating or they will be misread. First, the original wrapped-corpus 0.6B reached 3.172, which looks better than the unwrapped 0.6B's 3.329 and is not. Different corpus means a different held-out set, and the wrapped version's 5.7% newline tokens are near-deterministic after a 70-character line, so they deflate the loss with cheap wins. Removing them removed the easy tokens. It is a measurement artifact, not a regression. Second, a correction to my own earlier claim: I twice described the 0.6B run as still descending and undertrained at 3.172. Its series reads 3.176, 3.173, 3.172, 3.172 -- it flattened. All three runs plateau, so one epoch is about right for this corpus rather than short. The three-way booth puts 1.7B base, 1.7B tuned and 0.6B tuned side by side on the same nine prompts and seeds. The base arm is the control that matters: curly quotes go 0 of 18 on 1.7B base to 15 of 18 on 1.7B tuned, and worksheet-or- explainer collapse goes 3 of 18 to 0 of 18, so the shift is the adapter rather than the larger carrier. Hard-wrapping fell from 0.85 to 0.18, confirming the corpus unwrap carried through into the adapter. Sense partially returned. The 1.7B arm produces locally coherent sequential Victorian prose where the 0.6B produced word salad, but scene-level continuity still breaks mid-passage. One observation held loosely: curly quotes are slightly lower at 1.7B than 0.6B, which would fit a bigger model's stronger priors resisting the adapter at the same rank. That is untested and is not offered as established. |
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935b35ac2e |
Unwrap the Brontë corpus and launch the 1.7B rung
Operator: "start the 1.7b training." The 0.6B adapter learned the Gutenberg transcription's ~70-character line breaks along with the prose -- its output wrapped at a mid-length-line ratio of 0.85 against the base model's 0.00. That is typography rather than style, and every further rung would have inherited it, so the corpus is reflowed before rung 2 rather than after the sweep. The reflow joins 57,430 of 85,380 paragraph blocks and keeps 27,950. Verse is the hazard a blind join would destroy, so the decision is per block by median line length: blocks whose lines cluster near the wrap width are flowed prose, blocks of consistently short lines keep their breaks. Every kept multi-line block in the sample was genuinely verse with its lineation intact. No line ended in a lone hyphen, so the space-join could not split a word across lines. The acceptance check is content identity -- " ".join(text.split()) byte-identical before and after -- and it passed on all 852 records, proving only whitespace changed. Concrete cost of the old defect: 5.7% of the training budget was newline tokens. The same words pack to 5,210,112 tokens unwrapped against 5,525,504 wrapped. The 1.7B run is live at 159 steps and roughly 18.7 s/it. Everything but the carrier and the corpus is held from the 0.6B run: seed 4919, rank 32, lr 1e-4, seq 4096, batch 1 by accum 8, one epoch, eval and save every 25 steps so the minimum is located rather than assumed. That corpus change is a second variable and it is named as one. A 0.6B-vs-1.7B comparison is descriptive, not attributable, until the chained 0.6B rerun on the same unwrapped corpus lands behind it -- gated on the 1.7B actually producing an adapter, because a chain that fires on failure turns one lost run into two. "Did sense come back at 1.7B" is a within-arm reading and survives the confound; any between-rung delta does not. The original wrapped corpus is untouched, so the 0.6B run's pinned corpus sha 3959036cf851bf62 stays reproducible. |
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b8dbe71a1c |
memory: snapshot — R49 1-epoch pilot complete and awaiting adjudication; MeroMero A4B quantized, v2 blocked; althing 3.6.2 rolled
Ships the two pending code changes alongside the memory that describes them: train_voice_lora.py gains --eval-steps/--save-steps (the 3-epoch pilot overfit with per-epoch eval and save_strategy="no", so the minimum was neither visible nor recoverable), and the althing post-office compose is pinned to 3.6.2. Index rewritten: in-flight compressed from 176 lines to the live state, three detail files added for R49 D1-D3, the MeroMero four-failure chain, and the althing rollout. Seven closed pre-08-27 entries archived; the guards held back the rest because they carry open deferred pointers. |
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20bbb95113 |
fix(r49): loss-series collector silently dropped every eval record
The LossLog callback filtered on `"loss" in logs`. Trainer emits eval under `eval_loss` with no `loss` key, so every eval record was discarded and loss-series.json showed zero eval points while the log a metre away carried `eval_loss: 3.198`. An artefact that omits data which demonstrably exists reads as "no eval was run" rather than "the collector dropped it", which is the failure mode that costs someone a re-run. Collector now accepts either key. Seed 2 gets it from launch; seed 1 is already running with the old code, so recover_eval_series.py parses the eval records back out of its log -- the data was printed, not lost -- and the chain runs that recovery before cutting the generation arms, so the artefact is complete before anything reads it. The two seeds therefore differ in logging code but not in training math: the callback only affects what is recorded, never what is computed, so the weight trajectories remain comparable. Noting it because a difference between the two arms whose spread sets the decision threshold is worth stating even when it is provably inert. |
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8fff722f2c |
feat(r49): incumbent arm generated against the concrete gen seat, not the stale H02 name
brokkr-smithy corrected H02's incumbent naming: qwen3.6-35-a3b-heretic was retired from the gateway roster on 2026-08-15 and is not what Skaldsong would call today. Verified against the gateway and the seat itself -- alias `gen` resolves to hosted_vllm/qwen3.8-27b-uncensored on ana-ml2:8015, container vllm-gen, 262,144 ctx. The arm targets that. 24 records, style-prompted on the same prompts and sampler as the other arms. Alias resolved at run start AND end and confirmed stable across the run, per the fleet rule that an artefact records the backing model rather than the alias. Two things recorded rather than glossed: The harness is NOT matched to the other arms and the artefact says so. Base and adapted arms are local transformers on gx10; the incumbent is a served NVFP4 27B reached over the gateway, and it is an instruct model receiving a style instruction where the others are base models receiving none. That asymmetry is the comparison H02 asks for -- prompted imitation against trained voice -- but it must not be reported as if the harnesses were identical. The gateway echoes the ALIAS in each response's `model` field, so a row read on its own would have recorded "gen" as provenance -- the same class of mistake that inflated an exposure count 4.7x on this fleet. Rows now carry alias_echoed_by_gateway beside backing_model_resolved and its date, and the generator was fixed at source rather than only in the emitted file. Sanity: median 392 completion tokens, zero records opening with markdown or meta-commentary, output reads as continuation prose. The style prompt was written to be a fair incumbent rather than a strawman, since this arm is what the adapter must beat. |
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375244ad05 |
feat(r49): D2/D3 complete and the H02 pilot is training on gx10
Entity resolution, deterministic rename augmentation, packing and the pilot trainer. Qwen3-0.6B-Base is training now: 507 steps, 11.2 s/it, ~1h35m. D2 -- gender resolution is TITLE-FIRST, and that is a change from F02's method rather than a port of it. F02 used pronoun proximity and recorded that it is structurally blind to the first-person narrator, whose name appears mainly in dialogue surrounded by other people's pronouns. Measured here, proximity called JANE MALE -- the narrator of Jane Eyre and the single worst entity to get wrong. Titles have no such blind spot: Miss Eyre, Mrs. Fairfax, Mr. Rochester, Madame Beck, M. Paul, and a 19th-century novel is saturated with them. Measured: 16 entities resolved, zero wrong, every ambiguous case landing on HELD -- shared family surnames like Helstone and Pelet genuinely belong to both a man and a woman and hold as they should. Held means ungendered, not unrenamed. A HELD entity is still renamed, from the gender-neutral surname pool, because the operator's Yarros directive was "rename all proper nouns" and holding a place leaks it -- Thornfield appears 100 times in Jane Eyre and is as author-specific as Riders Quadrant was. Substituting a neutral token makes no gender claim, so no gender claim can be wrong. D3 -- pool is French + English per the operator, weighted per work by setting: Brussels novels 60% French, Yorkshire novels 25%. Locales restricted to fr_FR/fr_BE/en_GB/en_IE; en_US and en_AU carry modern surnames that are wrong register for the 1840s. The pool is filtered against Brontë's own 75-letter alphabet, so French accents stay and Czech/Latvian marks do not. Two collision defects found by running the leak gate rather than trusting it: `Burns` and `Marie` were drawn as replacements while being Brontë characters -- F02's collision filter was built against Yarros and does not carry -- and then `Pierre-Yves` passed a whole-string filter while `Pierre` (Mademoiselle St. Pierre) is a Villette character. The filter now compares by COMPONENT. Final gate: 0 of 203 source entities survive in any of 24 copy-files. Trainer records what the run RESOLVED to rather than what it requested -- attention implementation, dtype, device, corpus sha and harness cleanliness are read back off the live objects. transformers 5.x has dropped warmup_ratio, caught by reading the signature after the first launch failed on it; the 3% warmup is computed into warmup_steps instead. |
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ba8dac2c80 |
feat(r49): D1 corpus built and green — Charlotte Brontë, 680k words, 951k tokens
scripts/r49-corpus/{build_corpus,verify_corpus}.py; corpus staged at
gx10:~/r49-corpus/. Catalogue ids verified against gutenberg.org's own search
rather than recalled. Charlotte only -- the Bell poems are co-authored and the
Gaskell biography is a different hand, so neither belongs in a single-voice corpus.
Jane Eyre 1260 · Villette 9182 · Shirley 30486 · The Professor 1028
680,291 words · 142 chapters · 950,974 Qwen3 tokens (1.40 tok/word)
alphabet 75 letters, 23 non-ASCII · round-trip lossless · 0 byte-fallback
All 11 acceptance checks pass, including both tokenizer legs run against the pilot
carrier itself. With a real denominator the projections tighten: at 6 rename copies
x 3 epochs = 17.1M tokens, the 0.6B pilot is 1.98 h.
THE ALPHABET INVERTS THE YARROS RESULT. Brontë writes French constantly -- Villette
is set in a French-speaking city, Jane Eyre has Adèle, The Professor is set in
Brussels -- so the corpus carries é 432, è 237, à 93, ê 79, ô 48 plus œ and æ. F02
measured Yarros at 0.0002% non-ASCII and derived an ASCII-fold for the name pool.
Under F02's own subset rule the Brontë pool may keep FRENCH accents and must still
exclude the Czech/Latvian/Slovak/Hungarian marks that never appear here. The fold is
per-work, and this is the first corpus where deriving it changes the answer.
Typography was inconsistent across works and it was the transcriber, not the author:
Shirley uses straight quotes and `--` with zero em-dashes while Jane Eyre and
Villette use curly and em-dash. Normalised toward what the text means.
Three defects, each found by running something rather than reasoning about it:
`Produced by` matched Brontë's own prose four times, which is the adjective-"minor"
shape again and is fixed by anchoring boilerplate patterns to line start; asserting
open/close quote counts must be equal is wrong, because 19th-century multi-paragraph
speech legitimately runs a surplus of opens, so the real error signature is that no
paragraph may begin with a closing quote; and The Professor's table of contents puts
two chapter names per line, so a bare regex returns 38 headings for a 25-chapter
novel and a minimum-gap filter still leaks its tail -- the rule that works is that
the body's "CHAPTER I" is the last one in the file.
Records the operator's pilot ruling: trial on Qwen3-0.6B-Base first, move up only if
it produces something useful.
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