mlsanityData to 14 Oct 2026

A Certificate-Producing Cascade for Equational Implication: The SAIR EQT2 Stage 2 Solver

arXiv 2609.00706 ↗First posted 1 Sep 2026cs.CLcited by 0

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Abstract (arXiv)

The SAIR Mathematics Distillation Challenge on Equational Theories asks a solver to classify whether one magma identity implies another and, for either verdict, to return a certificate accepted by a deterministic Lean judge. We present a single-file solver organized as a cheapest-first cascade. Its false branch combines coefficient tests over structured algebra families, bounded finite-model search, an explicit central-groupoid witness, and several infinite-carrier witnesses. Its true branch is a proof-producing ordered unit superposition procedure with Knuth-Bendix ordering, bidirectional demodulation, indexing, memoised substitution, and anytime size deepening. Search results remain outside the trusted base: successful derivations are replayed as small Lean terms, and countermodels are rechecked by the competition judge. The frozen solver is a 189,504-byte Python file with SHA-256 f2392533c9f4c03b.... In local runs through official judge revision 2848228, it produced accepted certificates for all 1,889 rows of the six public sets with no language-model calls. Separate measurements recorded full agreement on the 800 published Stage 1 evaluation-distribution problems, 100 accepted rows in the canonical Marathon manifest without tokens, and 200 accepted rows in the hosted playground. These are regression and playground measurements, not a leaderboard result and not evidence about a hidden set. All quantitative claims are tied to immutable result ledgers; the paper makes no completeness or comparative-superiority claim.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 1 Sep 2026.

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References · 3

  1. 2019The Lean mathematical librarycited by 134
  2. 2025The Equational Theories Project: Advancing Collaborative Mathematical Research at Scalecited by 10
  3. 2025Distilling Many-Shot In-Context Learning into a Cheat Sheetcited by 2

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