a005a92fed
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).
120 lines
4.7 KiB
Python
120 lines
4.7 KiB
Python
"""Cross-domain capability index — AGI-roadmap Phase 1 (MEASURE).
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The yardstick that gates every later "more capable" claim. Two honest axes —
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**accuracy** (of committed answers; wrong stays 0 in assert mode) and **coverage**
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(attempted-not-refused) — aggregated across domains so it CANNOT be gamed by a
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narrow per-domain win: the headline coverage is the GEOMETRIC MEAN across domains,
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which only rises if *every* domain rises. A hack that maxes one lane and leaves
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the rest at zero leaves the geomean ~0.
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"""
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from __future__ import annotations
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from evals.capability_index.index import (
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DomainResult,
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aggregate,
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deterministic_digest,
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)
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def _d(domain: str, correct: int, wrong: int, refused: int) -> DomainResult:
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return DomainResult(domain=domain, correct=correct, wrong=wrong, refused=refused)
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def test_domain_result_axes() -> None:
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r = _d("logic", correct=8, wrong=0, refused=2)
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assert r.total == 10
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assert r.attempted == 8
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assert r.coverage == 0.8
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assert r.accuracy == 1.0 # of committed answers
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def test_aggregate_axes_micro() -> None:
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idx = aggregate([_d("a", 6, 0, 4), _d("b", 2, 0, 8)])
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assert idx.wrong_total == 0
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assert idx.coverage == 0.4 # (6+2)/(10+10) micro
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assert idx.accuracy == 1.0 # no wrong
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assert idx.breadth == 2 # both domains have some coverage
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def test_geomean_coverage_resists_narrow_gaming() -> None:
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# A NARROW hack: one domain maxed, the rest at zero coverage.
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narrow = aggregate(
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[_d("gamed", 10, 0, 0), _d("x", 0, 0, 10), _d("y", 0, 0, 10)]
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)
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# A BALANCED engine: every domain partially covered.
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balanced = aggregate(
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[_d("gamed", 4, 0, 6), _d("x", 4, 0, 6), _d("y", 4, 0, 6)]
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)
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# Micro-coverage is similar (~0.33 vs 0.40), but the geomean exposes the hack:
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assert narrow.coverage_geomean == 0.0 # any zero-coverage domain -> geomean 0
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assert balanced.coverage_geomean > 0.39
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# The capability score (geomean × accuracy) refuses to reward the narrow hack.
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assert narrow.capability_score == 0.0
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assert balanced.capability_score > 0.39
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def test_balanced_progress_moves_the_score_monotonically() -> None:
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low = aggregate([_d("a", 2, 0, 8), _d("b", 2, 0, 8)])
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high = aggregate([_d("a", 6, 0, 4), _d("b", 6, 0, 4)])
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assert high.coverage_geomean > low.coverage_geomean
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assert high.capability_score > low.capability_score
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def test_wrong_is_a_hard_gate() -> None:
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# In assert mode wrong MUST be 0; any wrong invalidates the index (score 0)
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# and is surfaced — never averaged away.
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idx = aggregate([_d("a", 8, 1, 1), _d("b", 5, 0, 5)])
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assert idx.wrong_total == 1
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assert idx.assert_mode_valid is False
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assert idx.capability_score == 0.0 # wrong=0 is non-negotiable in assert mode
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def test_digest_is_deterministic_and_bites() -> None:
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a = aggregate([_d("a", 6, 0, 4), _d("b", 2, 0, 8)])
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b = aggregate([_d("a", 6, 0, 4), _d("b", 2, 0, 8)])
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assert deterministic_digest(a) == deterministic_digest(b)
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moved = aggregate([_d("a", 7, 0, 3), _d("b", 2, 0, 8)])
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assert deterministic_digest(moved) != deterministic_digest(a)
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def test_empty_index_is_well_defined() -> None:
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idx = aggregate([])
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assert idx.coverage == 0.0
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assert idx.coverage_geomean == 0.0
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assert idx.breadth == 0
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assert idx.capability_score == 0.0
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def test_real_lanes_compose_into_the_index_with_wrong_zero() -> None:
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# The baseline: three structured-input reasoning lanes PLUS the four
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# comprehension lanes (prose -> MeaningGraph -> projection -> independent
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# oracle) compose into the cross-domain index with zero wrong commits.
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from evals.capability_index.adapters import collect_domain_results
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collection = collect_domain_results()
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assert collection.not_covered == () # every adapter ran (no silent drop)
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idx = aggregate(list(collection.results))
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assert idx.wrong_total == 0
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assert idx.assert_mode_valid
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assert idx.breadth == 8
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assert {d.domain for d in idx.domains} == {
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"deductive_logic",
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"dimensional",
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"relational_metric",
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"comprehension_set_membership",
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"comprehension_syllogism",
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"comprehension_total_ordering",
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"comprehension_propositional",
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"comprehension_relational_metric",
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}
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assert idx.capability_score > 0.5 # real, non-trivial cross-domain capability
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def test_index_report_is_deterministic_across_runs() -> None:
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# The capability number is reproducible — improvement is a replayable curve.
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from evals.capability_index.adapters import collect_domain_results
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a = deterministic_digest(aggregate(list(collect_domain_results().results)))
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b = deterministic_digest(aggregate(list(collect_domain_results().results)))
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assert a == b
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