GSM8K Math Evaluation Lane Contract

What it measures

This lane measures the mathematical reasoning capability of the CORE cognitive engine over grade-school word problems, specifically evaluating the integration of:

Natural language problem parsing (generate/math_parser.py -> MathProblemGraph).
Graph-based deterministic solution generation (generate/math_solver.py -> SolutionTrace).
Solution trace verification (generate/math_verifier.py).

Why it matters

Mathematical reasoning requires chaining logical and state-mutating operations deterministically. This lane enforces that the cognitive engine can parse, represent, solve, and verify arithmetic problems without relying on stochastic generation or approximation.

By using an original, curated set of 200 problems matching our own vocabulary and grammar, we can safely benchmark development without contaminating the sealed GSM8K holdout test set.

Splits and ID Schema

Dev Set: 50 cases (gma-001 ... gma-050). Authored manually to establish CORE grammar templates.
Public Set: 150 cases (gma-101 ... gma-250). Synthetically generated via scripts/generate_gsm8k_public_corpus.py using grammar templates (e.g., transfer, divide, proportional_change.decrease_to_fraction) established by the dev set.
Holdout Set: Sealed, loaded dynamically in future phases.

Metrics

The lane runner enforces three gate checks per case:

M1. Parse Correctness: parse_problem(problem_text) yields a graph whose canonical bytes are identical to the ground-truth graph.
M2. Solve Correctness: solve(graph) yields a trace with answer_value and answer_unit exactly matching the expected answer and unit.
M3. Verification Correctness: The trace successfully replays via the verifier to reproduce the answer value and unit.

Pass Thresholds

Total Success Rate: 100% of authored dev and public cases (200/200) must satisfy M1, M2, and M3.
Replay Determinism: 100% trace determinism.

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