arXiv AI By Johannes K. Fichte, Stefan Szeider

count-catcher: Lean Theorems from Model Counting Certificates

Read the original on arXiv AI →

count-catcher is a tool that transforms a CPOG certificate produced by the exact model counter d4 into a Lean theorem asserting that a CNF formula defined in Lean has exactly N models over V variables. It verifies the equivalence between the formula and a Boolean circuit via forward and reverse checks, using Lean’s LRAT checker and formally verified computations. The resulting theorems are combined with Mathlib results, such as Burnside’s lemma, to count combinatorial objects—for example, proving the number of Latin squares of order 8 as a weighted sum of certified counts from terabytes of certificates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Oct 26

SemantEq: Faithful Bidirectional Equivalence of Formal Statements

SemantEq introduces a principled method for verifying that formal statements faithfully translate natural‑language mathematics by requiring each statement to transform into the other through a chain of allowed logical steps, rather than relying on trivial logical equivalence. The system evaluates proofs using Lean’s proof terms, achieving 100 % precision and 65.6 % recall on the ProofNetVerif test split, outperforming prior methods. A complementary audit tool, SemantEq‑ProofAudit, further improves recall for equivalent pairs while rejecting all non‑equivalent pairs.

By Xinrui Li, Pawan Sasanka Ammanamanchi, Siddharth Bhat, Rianna Jamdar, Yongxi Lin, Stella Biderman
arXiv Machine Learning
Sep 21

The Refutation Gap: Certifying Both Halves of an Optimality Claim

The paper addresses the asymmetry in verifying optimality claims for synthesis pipelines, distinguishing between the upper bound (existence of a program) and the lower bound (non-existence of a smaller program). It introduces a pipeline that synthesizes minimal linear straight‑line programs over GF(2) and produces DRAT proofs for every UNSAT result, thereby closing the so‑called refutation gap for 121 previously uncertified optimality claims. The authors report that the median proof size is 1.1 MB, checking takes 1.9× the solving time, and that their verification process uncovered defects missed by code review, highlighted interface obstacles, and exposed a budget‑related audit failure.

By Rohan Pandey