A1 of the refined sequencing — the binary-relation reader was inert w.r.t. the
yardstick (contributing 0). This adds a comprehension_relational_predicate domain:
binary-relation prose scored against hand-authored independent gold (predicate,
subject, object) triples — INV-25 independent / INV-27 reader-disjoint (the reader
never produced the gold). Index breadth 8->9, capability_score 0.937258->0.944030,
wrong_total still 0; baseline.json re-frozen to digest 1ea91c1e.
Rigor split: the index lane is POSITIVE-ONLY (clean coverage, consistent with the
other 8 lanes — mixing adversarial refuse-cases into the coverage denominator would
make 'added capability' read as a score drop). The #596 fabrication-catch lives in a
dedicated falsification test (evals/relational/v1/refusals.jsonl): the trailing-
qualifier / dangling-copula / negation / verb-form cases MUST refuse — bites if the
reader ever fabricates. Honest coverage gap recorded: overlaps_event has no copular
surface form (verb-form 'A overlaps B' refuses), so 17 positives cover 15/16 predicates.
The binding-graph's FIRST comprehension consumer (doctrine-aligned: quantities live
in binding_graph, NOT the MeaningGraph). generate/quantitative_comprehension.py
reads arithmetic prose into SymbolBinding/BoundFact/BoundEquation and runs the REAL
check_admissibility (shell -> verify -> rebuild with the actual UnitProof) — there
is NO stamped "admitted": an equation is admitted only if its operand units verify.
Then to_relational_metric projects the binding-graph to the independent
relational_metric oracle for the verdict.
Templates (digits only; non-digit quantity REFUSES):
"<X> has <N> <unit>" -> BoundFact(X = N)
"<Y> has <N> more <unit> than <X>" -> BoundEquation(Y = X + N) op=add
"<Y> has <N> fewer <unit> than <X>" -> BoundEquation(Y = X - N) op=subtract
"How many <unit> does <Y> have" -> ask Y
"How many <unit> do <X> and <Y> have"-> total = X + Y; ask total
Unit modelling (honest, not faked): a noun the closed en_units_v1 pack knows is
used verbatim (dollars -> dollar/money); an UNKNOWN sortal noun (stickers, coins)
is a count of discrete objects -> the existing 'item' lemma (dimension count). So
admissibility stays a REAL check: count+count admits, count+money (a mixed-unit
sum) REFUSES with unit_mismatch — verified to bite.
comprehension_relational_metric: 15/15 wrong=0 (full coverage). Located OUTSIDE
generate/meaning_graph (it targets binding_graph, not the MeaningGraph) so INV-28
neutrality stays intact; oracle imports none of the SUT (new INV-25 lane).
Capability index breadth 7->8, score 0.928622 -> 0.937258, wrong_total 0, digest
50e0675b…
Tests: reader templates + count/known-unit modelling + admissibility-bite (mixed
unit refuses) + non-digit refusal; end-to-end full-coverage wrong=0; arithmetic
added to the structure-preservation generative panel (projected relations+query ==
ground truth); capability breadth 7->8; INV-25 arithmetic lane. 93 targeted + 90
smoke green; lane SHAs 8/9 (sole miss = public_demo env flake; deductive_logic +
math_teaching unchanged -> no GSM8K coupling).
Adds comprehension_propositional — the comprehension organ now reads the classic
propositional ARGUMENT FORMS end-to-end into the flagship deductive_logic ROBDD
oracle (the most robustly independent gold in the repo). The neutral MeaningGraph
now feeds FOUR independent oracles (set-membership, syllogism-validity,
total-ordering, propositional-entailment) from one interlingua — the Option-B
interlingua thesis validated.
reader.py: propositional templates (atoms are chunked NP ids; fits the existing
entities + n-ary relations + negation model — NO interlingua change, propositional
is not arithmetic-quantities):
- "if <P> then <Q>" -> implies(P, Q)
- "not <P>" -> asserted(P, negated=True)
- "<P> or <Q>" -> or(P, Q)
- "<P>" (single token) -> asserted(P) (bare-atom, single-token only to
keep the parse-or-refuse floor)
- "therefore <prop>" -> query of the same predicate
Relations now carry a negated flag end-to-end (asserted negation).
projectors.py: to_deductive_logic serializes propositional relations/query into
formula strings (keyword operators the oracle tokenizer accepts); returns None
(refusal) unless the comprehension is purely propositional, so categorical/ordering
comprehensions never leak into the entailment oracle.
evals: new evals/propositional_logic/v1 (12 cases — modus ponens/tollens,
hypothetical & disjunctive syllogism, the affirming-consequent / denying-antecedent
fallacies which the oracle marks "unknown"; gold = oracle verdict) + gold-only
runner + evals/comprehension/propositional_runner.py. Oracle "refused" (formula
unevaluable) is treated as a decline, never a wrong.
Scores: comprehension_propositional 12/12 wrong=0 (full coverage); no regression on
the 3 existing lanes (8/8, 7/8, 7/8). Capability index breadth 6->7, score
0.917231 -> 0.928622, wrong_total 0, digest 51df7bba…
Tests: reader propositional templates; to_deductive_logic projector tests;
end-to-end full-coverage wrong=0; propositional generative round-trip added to the
wrong=0 property suite (verified to BITE under a reversed-implies mutation);
capability breadth 6->7. 115 targeted + 87 smoke green. Lane SHAs 8/9 (sole miss =
public_demo env wall-clock flake; deductive_logic_v1 unchanged).
Recovers the multi-word-NP cases the reader previously refused, by adopting ONE
principled canonicalization contract (evals/comprehension/CANONICALIZATION.md) that
the reader AND the gold lanes both follow — so a committed answer can only match
gold or refuse, never silently mean something else.
Contract: a noun-phrase slot -> tokens lowercased, joined with "_"; a plural class
slot singularizes its head first ("metal objects"->"metal_object",
"North station"->"north_station", "Level one"->"level_one"). JOIN is chosen over
head-word-only ("metal objects"->"metal") because head-word-only is
information-destroying — it collapses "metal objects" and "metal tools" into one
false identity, itself a wrong=0 hazard.
reader.py: slot-based templates chunk multi-token NPs (_chunk / _chunk_class
replace the single-token _one / _one_class). Reserved-function-word guard fires only
INSIDE a multi-token slot (a lone "A" item is content, not the article). Still
parse-or-refuse: reserved-word leaks ("Compare beta with beta in the same order"),
non-pluralizable class heads (adjectival "trained"), and the ambiguous adjacent
two-NP subset query ("Are all <Xs> <Ys>?") all REFUSE.
gold (the contract update, logic-preserving — only term NAMES change):
- sy-v1-0008: metal/soft -> metal_object/soft_object (was head-word-only)
- to-v1-0005: red -> red_rank (was head-word-only)
- to-v1-0004: prose made internally consistent ("is after", "north station") +
north -> north_station (original prose used "North station" in the fact but
"north" in the query — a latent inconsistency)
- to-v1-0007: already conformed (level_one…), no change
Gold-only integrity runners stay 8/8 both lanes (structure+query+gold consistent).
Scores: set_membership 8/8, syllogism 6/8->7/8, total_ordering 4/8->7/8, all
wrong=0. Capability index re-frozen: score 0.814356 -> 0.917231, breadth 6,
wrong_total 0, digest 13d7db6c…
Tests: reader chunking + refusal tests; multi-word generative round-trip added to
the wrong=0 property suite (verified to BITE under a head-word-only mutation —
collapsed ids produce a wrong verdict the test catches); pinned counts updated.
100 comprehension/capability targeted + 87 smoke green.
Phase 2a r2/r3/r4 of the redefined plan: the general comprehension reader now
reads THREE independent-gold reasoning domains end-to-end (prose -> MeaningGraph
-> projection -> independent oracle -> answer vs gold), all wrong=0, and all
three are wired into the capability index.
reader.py — new domain-agnostic templates (function words + order; parse-or-refuse):
- categorical E/I/O: "no Xs are Ys"->disjoint, "some Xs are Ys"->intersects,
"some Xs are not Ys"->some_not (A "all Xs are Ys"->subset already existed)
- "therefore <categorical>" -> conclusion QUERY (same neutral predicate vocab)
- comparative facts: "<X> [is] <comp> [than] <Y>" -> less(...), closed
less/greater comparator lexicon, elided-copula support
- sort query ("sort ascending|descending", "... order from <low> to <high>")
and compare query ("compare <X> with <Y>")
- clause-splitting on commas / leading and|or for multi-clause sentences
projectors.py — to_syllogism (premises + validity conclusion, finite-model size 3)
and to_total_ordering (less-facts + sort/compare). Both return None when nothing
is honestly askable of their oracle (caller treats as refusal).
capability_index — wire 3 comprehension lanes into ADAPTERS; re-freeze baseline
breadth 3->6, capability_score 0.919641->0.814356 (geomean falls BY DESIGN as
honest partial-coverage domains join; wrong_total stays 0). digest 0a98b9b4...
Scores: set_membership 8/8, syllogism 6/8, total_ordering 4/8 — all wrong=0.
Multi-word NP handling is DEFERRED on purpose, not missed: the gold lanes
canonicalize multi-word NPs three contradictory ways ("North station"->"north",
"Level one"->"level_one", "metal objects"->"metal"), so no single general rule is
wrong=0-safe. The reader refuses multi-word NPs until the gold lanes carry a
canonicalization contract. Every refusal is a genuine harder phenomenon
(multi-word NP, adjectival predicate, trailing tokens) — never a readable case
silently dropped.
Tests: reader templates, projector unit tests, syllogism/total_ordering
end-to-end wrong=0 with pinned counts, capability breadth 3->6. 138 targeted +
87 smoke green. Lane SHAs 8/9 (sole miss = public_demo env wall-clock flake).
Disciplined Path β (field decode α was empirically falsified). Reads S-P-O
structure SYMBOLICALLY from the token sequence via domain-agnostic templates
keyed on FUNCTION WORDS + ORDER, mints content as MeaningGraph entities/relations,
parse-or-refuse (wrong=0 at the comprehension layer).
Templates (set_membership): 'X is a Y' -> member; 'all Xs are Ys' -> subset;
'is X a Y?' / 'are all Xs Ys?' -> queries; definite-NP ('the X is a Y');
conservative singularization incl. irregulars (people->person), refusing
unknown morphology rather than guessing.
End-to-end: prose -> comprehend -> project -> INDEPENDENT set_membership oracle
-> answer vs gold. Result on the real v1 lane: 8 correct / 0 wrong / 0 refused
(full coverage, wrong=0). Cross-content generality (animals/professions/geography)
asserted. 14 reader unit tests + 3 end-to-end tests, each bites under its
violation. Additive only; invariants + capability index green.
