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).
The instrument that gates every later "more capable" claim and makes "general,
not narrow" a number. evals/capability_index/ composes the self-loading
independent-gold reasoning lanes (deductive_logic, dimensional, relational_metric)
into one report with honest, un-gameable axes:
- accuracy (of committed answers; wrong stays 0 in assert mode),
- coverage (attempted-not-refused),
- coverage_geomean — the headline: geometric mean of per-domain coverage, which is
0 if ANY domain has zero coverage, so a narrow per-domain win cannot move it; it
rises only when breadth rises,
- capability_score = coverage_geomean × accuracy, HARD-GATED to 0 if any domain
committed a wrong answer (assert-mode invariant),
- a deterministic digest (the replayable baseline the autonomous loop must climb).
Baseline (today): score 0.9196, accuracy 1.0, breadth 3, wrong_total 0 — high
because all three composed lanes are formal/structured; when comprehension-gated
NL domains join, the geomean will honestly drop to expose the breadth gap (the
instrument working). Adapters surface any lane that fails to run as not_covered —
no silent drop (proven: it caught a deductive-report shape mismatch mid-build).
Pure aggregation + the geomean anti-gaming property + the wrong=0 hard gate are
unit-tested; a real-composition integration test asserts wrong=0 + breadth=3.
10 tests + 52 architectural invariants pass. Additive (new evals/ package).
Part of docs/analysis/AGI-candidacy-autonomous-improvement-roadmap-2026-06-05.md (Phase 1).
