Phase 5 item 1 asked for the overlap to be sized "by measuring the reader's
construction set against the writer's rather than by growing corpora blindly".
New lane `evals/construction_inventory` does that: it sweeps the writer's whole
parameter space (every RhetoricalMove x IntentTag x predicate x quantifier x
tense x aspect, both public entry points, 32292 cells), quotients it by the
reader's OWN function-word skeleton, and comprehends each construction under
three vocabularies.
writer constructions 1739
reader constructions 19 (mint-site AST-guarded)
overlap, faithful 6
accepts but MIS-READS 22
refuses 1711
vocabulary-dependent 0
Faithfulness, not acceptance, is the criterion — and that reverses two claims
in the plan's §6 RESULT, using a metric that was already on the page:
* g_read_rate is 1/293 but **g_args_rate is 0.0**. The one surface that "reads",
`all molecules are defined as compounds`, is comprehended as
`subset(molecule, defined_as_compound)` — the reader chunked the writer's verb
phrase into a class name. It accepted; it did not comprehend.
* "one construction wide" was wrong both ways: zero on that corpus, six over the
writer's actual output space. A corpus cannot report an inventory's size.
Three findings re-order Phase 5 item 1:
1. The reader FABRICATES on 22 constructions — neither reads nor refuses.
`every dog is a mammal` -> member(every_dog, mammal);
`furthermore, all dogs are mammals` -> asserted(furthermore).
Both are ordinary user English, not writer artefacts. Root cause: _RESERVED
lacks the function words the writer emits, and _parse_propositional accepts
any single token as a fact.
2. It reaches SERVED output. deduction_surface recites
`Given: furthermore; p implies q; p.` — a premise the user never stated — and
chat/runtime.py realizes declarative turns into the held self, so a
fabricated atom is vault-writable. Widening the inventory first would widen
the fabrication surface with it.
3. ADR-0265's defect class survives inside ADR-0265's designated owner of clause
grammar: the four aspect arms of _inflect_predicate bind `negated` to a
wildcard and never read it, so `dog has been defined as mammal` serves both
the assertion and the denial (10530/16146 points). Unreachable today — no
producer sets aspect — so a loaded gun, not a casualty. It survived because
ADR-0265's invariant is structural (is `negated` threaded?) and cannot see an
arm that receives the flag and ignores it. A behavioural sweep can.
The two fixes are written already, as the mutations that turn the defect pins
red. They are NOT applied here: they change what CORE comprehends from user
input, which is a serving change on the truth path — authorization-gated, ADR
first, on the ADR-0261 §5.1 refuse-don't-drop precedent.
Guards, so the tables cannot rot: reader constructions are pinned to an AST
count of the reader's 10 mint sites; the declared tense/aspect axes are pinned
to _inflect_predicate's match arms; the committed corpus is pinned to the
fillers in use. 14 pins, 13 mutations observed RED.
[Verification]: in-worktree, CPython 3.12.13, uv sync --locked.
deductive 517 (was 503, +14 — count moved, registration confirmed)
smoke 641 (unchanged; the file is registered in `deductive`)
lane SHAs 11/11 match, no pin edited
pyright 0 errors on all new files
mutations 13/13 red, including both fabrication fixes
Phase 4 of the grammar-unification arc, resolved by option (b) of the plan.
The problem: 149 green fluency cases scored `realize_target`, and
`core/cognition/pipeline.py` never calls it -- it calls `realize_semantic`.
`english_fluency_ood` reported 117/117 + 39/39 for a function that does not
speak, for the whole life of the lane.
The plan framed this as "which realizer is better". Reading the source says it
is not a quality question. `render_semantic`'s signature is
`(intent, subject, predicate, obj, secondary, language, root)` -- no `negated`,
no `quantifier`, no `tense`, no `aspect` -- and `realize_semantic` never reads
them off the step. So the serving writer cannot express content the
ArticulationStep is carrying:
negated=False -> 'Knowledge is defined as opinion.'
negated=True -> 'Knowledge is defined as opinion.'
It serves the AFFIRMATIVE of a negated proposition. That is the ADR-0261 §5.1
family, not a fluency defect. Pinned here as a defect; NOT fixed, because
fixing it changes served output and belongs to Shay.
Measured -- identical contract, all seven scored corpora
--------------------------------------------------------
bucket n realize_target realize_semantic
feature-bearing, single node 214 207 49
no features, multi-node 100 100 3
no features, single node (CONTROL) 33 33 33
total 347 340 85
The control is what makes the rest mean anything. Every corpus hardcodes
IntentTag.UNKNOWN, so "the serving writer scores badly" could have been an
artifact of never giving it a real intent. On the 33 cases carrying nothing it
cannot express, the two are IDENTICAL -- so the gap on the other 314 is the
dropped features and the clause joining, not the intent.
Delivered
---------
- `grammatical_coverage/runner.py` reports `serving_accuracy` beside
`accuracy`; `english_fluency_ood` delegates to that run_lane and gains it
for free. The realizer is a parameter now instead of a hardcoded import.
- `tests/test_phase4_realizer_resolution.py` -- the control, the decomposition,
the negation defect pin, and the §6 evidence.
- The claim is restated at both places it was made: the lane docstring and
`grammar_roundtrip/contract.md`.
Zero served bytes change. No serving authorization needed.
§6 -- the plan's pre-commitment was wrong, and is corrected rather than
quietly edited
------------------------------------------------------------------------
§6 forks on read_rate: risen => Phase 5 diversity; near-zero => §1.8 graph-model
mismatch => ADR. It pre-committed to the second. Neither is what the
measurement says.
g_read_rate went to 1/293. The unblocked case was blocked by a one-line WRITER
defect (predicate-nominal object agreement, #135), not by §1.8. The other 292:
no_template_match 289 reader has no SUBJ-VERB-OBJ template at all
unknown_morphology 2 prepositional objects (reserved_word_in_np)
unsupported_negation 1 reader has no negated-categorical template
Every one is the reader declining a CONSTRUCTION, not a projection disagreeing
about a graph it parsed. Where a construction is in both inventories the round
trip closes exactly. So the barrier is the OVERLAP of the two construction
inventories, currently one construction wide -- Phase 5's item 1, tractable,
not an ADR-scale model decision.
§1.6 read "uniform no_template_match" as evidence FOR the type mismatch. It is
not: no_template_match is a coverage fact, and the corpus was 289/293 bare
transitives, a construction the reader never claimed to read. The measurement
was mostly reporting the corpus's composition.
And a finding in my own stack
------------------------------
`tests/test_realizer_quantifier_agreement.py` lived ONLY in the `cognition`
suite, which is not on the AGENTS.md pre-push gate. So every pin Phases 3 and 4
added to it -- including the invariant covering all twelve inflection branches
-- ran in NO gate. That is why smoke stayed at 621 across two PRs that added 13
tests between them, and it is the same silent-red shape the smoke list already
calls out for test_adr_index.py.
Registered into `deductive`, which now runs 504 instead of 406.
Mutation
--------
baseline 12 pass
serving metric computed with realize_target 4 FAIL
render_semantic GAINS a `negated` parameter 2 FAIL
realize_semantic delegated to realize_target 6 FAIL
Row 2 matters most: if someone FIXES the negation defect, the pin forces a
deliberate revision instead of passing silently.
Still open, still Shay's: option (a), promoting realize_target to the serving
path. It now has a price tag -- 340/347 over 85/347 and the ability to say
"not", against a move in every surface hash and whatever the Shadow Coherence
Gate ruling in core/cognition/surface_resolution.py was protecting.
[Verification]: in-worktree on CPython 3.12.13 with `uv sync --locked` --
smoke 621, deductive 504 (was 406; +12 new Phase 4 pins, +86 previously
ungated), lane pins 11/11 unchanged, no pin edited.
_inflect_predicate applied base_form — a SINGLE-VERB function — to whole
humanized predicate phrases, stripping the final word's last character
class, and its plural branch never consulted `copular`. Nine of the 26 seed
predicates came out wrong and every multi-word one did:
is defined as -> "is defined a" want "are defined as"
has the following ... -> "...step" want "have the following steps"
belongs to -> "belongs to" want "belong to"
causes -> "caus" want "cause"
Root causes, both single-point as §1.4 predicted:
1. morphology.agree_plural_phrase inflects the HEAD (the finite verb is the
first token of every humanized predicate) and carries the rest through.
base_form stays single-verb and is PINNED as still wrong on a phrase, so
nobody "fixes" the symptom in the wrong place.
2. The plural branch now agrees the phrase. The plural NEGATED branch had the
same defect ("do not is defined a") and is fixed with it: a plural copula
takes a bare "not", everything else takes do-support.
Two further defects found while writing the eval cases, both fixed here:
base_form("causes") -> "caus". The -es sibilant rule fired on a stem
ending in a single "s"; it must require a doubled "ss" ("passes"->"pass"),
because a single "s" is nearly always a stem ending in "e" that took a
plain -s ("causes"->"cause").
pluralize("proof") -> "prooves". f/fe -> ves is NOT productive in English
(proof->proofs, chief->chiefs, roof->roofs); it is a closed set. Now
derived as lexicon.VES_PLURAL_SINGULARS from the ves-rows of
IRREGULAR_SINGULARS, so the rule cannot claim a word the table does not
know. Phase 2B had put pluralize on the SERVING path, so this one was live
— and the surface-hashing pin from #133 confirms no served surface moved,
which is that guard's first real use.
New construction C14 quantified_copular, 13 cases: the combination that was
broken was the one never tested. Measured before: 152 corpus cases carry a
quantifier and ZERO combine it with a copular predicate.
MY FIRST DRAFT OF THOSE CASES COULD NOT FAIL. must_contain/word_order listed
quantifier, subject and object but NOT THE VERB, so "all molecules is
defined a compound" passed and reverting the fix left 47/47 green. Rewritten
with the agreed verb in both constraints plus reject_surfaces carrying the
ACTUAL pre-fix output, computed by running the reverted code rather than
guessed. Mutation now:
baseline 13/13 C14
revert phrase-head agreement 4/13
revert -es stem rule 12/13
revert closed ves set 11/13
The 4 survivors of the first mutation are the mass-noun controls, which is
correct — "all evidence is grounded in truth" must NOT pluralize.
Same lesson as the lane pins in #133: a pin that cannot fail guards nothing,
and the only way to know is to break the thing on purpose.
grammar_roundtrip's graph corpus is harvested from the committed case files,
so it grew 280 -> 293. Updated exactly rather than loosened to an inequality:
a corpus that grows or shrinks should require a deliberate edit.
[Verification]: in-worktree on CPython 3.12.13, uv sync --locked —
agreement 26/26 (from 17/26); english_fluency_ood 117/117 + 39/39 + dev
13/13 unchanged; grammatical_coverage v1 49/49; smoke 621 unchanged;
deductive 405; scripts/verify_lane_shas.py 11/11, no pin edited.
The measurement foundation for docs/plans/grammar-unification-2026-07-26.md.
WHY: evals/deterministic_fluency reports 1.00 on all six predicates and
still passes "banana does the.", "wet ground rains the is." and
"is is is is." — it checks terminal punctuation, presence of a verb-shaped
token, and two anti-shape regexes. Heuristic predicates will always have
that failure mode, because grammaticality cannot be measured without a
grammar. So this lane measures agreement between the two halves of CORE
that already encode grammar, and requires the measurement to FAIL on salad.
Two directions, reported separately because they fail for different
reasons and have different remedies:
G-round-trip graph -> realize_target -> surface -> comprehend -> graph
S-round-trip surface -> comprehend -> graph -> categorical renderer -> surface
v1 baseline on main @ 9696443a:
graph_cases 280 surface_cases 8
g_write_rate 1.000 s_read_rate 1.000
g_read_rate 0.000 s_renderable_rate 0.625
g_exact_rate 0.000 s_surface_match_rate 0.000
negative_cases 16
reject_rate 1.000
g_read_rate and s_surface_match_rate are pins on measured DEFECTS, not
goals; they may be revised upward only. s_surface_match_rate = 0 is the
§1.7 categorical render defect caught by construction — the lane found it
without being told to look.
g_args_rate and g_predicates_rate are deliberately separate: high argument
agreement with low predicate agreement would mean the grammars align and
only the vocabulary is split, a materially different remedy from both
being low. That distinction decides the arc's direction (plan §6).
Design notes:
- The committed cases.jsonl is the SINGLE source for authored surfaces —
no in-module duplicate, since a second copy of a corpus is the defect
this arc exists to remove. Negative shuffles are DERIVED at run time so
they cannot drift from the positives.
- The shuffles are lexically identical to positives (same vocabulary, same
length, order destroyed) so the lane cannot pass by vocabulary-checking.
- Fixed rotation, not a PRNG, so reject_rate is byte-reproducible.
- The lane keeps a local copy of the reader's quantifier map ON PURPOSE so
it never becomes a consumer of what it measures;
test_quantifier_map_matches_reader fails loudly if the reader changes.
- _render_categorical deliberately reaches a private serving function: a
lane measuring a private copy would measure what users never see.
Every guarantee is paired with a mutation test. The load-bearing one is
test_reject_rate_goes_red_when_the_reader_accepts_everything: an
accept-everything reader must drive reject_rate to 0.0. Without it,
reject_rate == 1.0 would be unfalsifiable — precisely the defect that
makes the existing fluency lane decoration.
Also documents plainly what round-trip does NOT prove: it measures mutual
intelligibility, not English quality. english_fluency_ood accepts "river
flows valley" and round-trip would be happy with it. No metric here may be
cited as evidence of prose quality.
scripts/measure_grammar_seam.py reproduces every number in the plan's §1
so a reader can check them instead of trusting them.
[Verification]: in-worktree on CPython 3.12.13, uv sync --locked —
smoke 621 (unchanged), deductive 364 (349 + 15 new). Lane SHA pins
verified separately. No serving code touched; new files plus one suite
registration line.
Shay