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原文摘要

We present Moxia, a trust-first neuro-symbolic architecture for self-explaining mathematical reasoning over natural-language input. Its language model is strictly a canonicalizer: it rewrites informal problem text into a narrow schema consumed by a deterministic Computer-Algebra-System (CAS) pipeline, which derives the answer or abstains as a first-class output. Routing follows a 1:1:1 alignment of problem-shape regex, schema-specific prompt, and closed-form handler: 5,741 routes, 75.9% of which answer without invoking the model, with zero LOST_CORRECT regressions as a standing release gate. Because the answer is derived, so is its explanation, rendered from the steps the handler took; where the derivation fits a fragment of Lean 4 with Mathlib, the same declared data become a theorem the kernel checks. On the full 5,000-record MATH test split, designed against, Moxia answers 95.6% (4,780/5,000) with zero confident-wrong answers and abstains, saying where, on the other 220; its MATH-500 subset reads 95.2% (476/500), also with zero wrong. Earlier versions called MATH-500 held out; it is a subset of the test split, and this version says so. We release the run record by record: the problem, the expected answer, the model's transcription, the answer or abstention, the explanation, and the Lean theorem marked as compiled. 3,142 of the 4,780 correct answers carry a theorem, and all 3,142 compile; building the theorems actually served, not only a fixture corpus, found seven that did not, two of them false statements beside correct answers, all repaired before release. What we emphasize is not an accuracy figure but the forward dynamic: every logged abstain is a candidate correct after one ship cycle, since new tasks compose without regressing the registry.