A three-artifact approach is proposed: mathcomp-style Rocq backed by an explicit coding-style skill, a Lean-style blueprint cross-linking informal math to formal lemmas, and a verification PDF pairing each definition and theorem statement with its informal version.
ProofJudge is introduced, an agentic LLM-as-judge system that scores formal proof quality along five dimensions beyond correctness: library leverage, automation fit, structural clarity, statement quality, and Mathlib conventions.
AoA lifts the agent off source text and onto the abstract syntax tree (AST): the model supplies proofs as JSON representations of Minilang's AST and drives the prover through a tree-edit model that fuses proof operations and states into one proof tree, so each operation carries its own subgoal's state, readable directl...
Qiyuan Xu, Joshua Ong Jun Leang, Renxi Wang et al.· arXiv.org· 0 citations
This pilot study evaluated six open-weight LLMs on the same 100 theorems from CoqStoq, a benchmark derived from real Coq projects and did not statistically test whether one model outperforms another, therefore, the results do not establish a universal ranking of the six models.
A. Ryan, Md Erfan, Akond Ashfaque Ur Rahman et al.· 0 citations
ITPEval is presented, the first benchmark for evaluating automated formal proof translation across four major ITPs (Lean 4, Rocq, Isabelle, and HOL Light), spanning two distinct logical foundations and itpeval, a unified multi-ITP verification infrastructure with state-isolated warm backends that preserve per-artifact...
Jiayi Wu, Robert Joseph George, Anima Anandkumar· 0 citations
As large language models become increasingly capable of generating mathematical arguments, mathematics is likely to face not a scarcity of proofs but an abundance of plausible ones. In such an environment, verification, exposition, and incorporation into reusable mathematical infrastructure become central tasks. We rep...
Shuoming Deng, Kenneth W. Shum· arXiv.org· 0 citations
This work introduces MathAdv, a diagnostic benchmark spanning 13 domains across undergraduate- and graduate-level mathematics, and shows how component-wise evaluation can reveal model capabilities and failure modes that aggregate theorem-proving accuracy obscures.
Jiajie Yuan, C. Lockhart, Xiao-Yun Liu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
