The paper introduces a framework that uses large language models to represent and measure mathematical arguments, capturing statements, equations, assumptions, and constructions while distinguishing source occurrences from contextual states and inference records. It defines a directed acyclic graph representation where depth is the longest prerequisite path and bulk counts distinct ancestor edges, providing a recurrence that handles shared subarguments and preserves macro skeletons. The framework includes a standardized, versioned prompt protocol for annotation discovery, deterministic recomputation, and review assistance, and proposes cross‑paper links to study theorem co‑use and support probabilistic premise suggestions and review of omissions.
By Eunji Park
arXiv:2606. 08728v1 Announce Type: new Abstract: Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequential AI frontiers.
By Syed Rifat Raiyan, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
LeanGraphSearch is a graph‑based retrieval method for the Lean 4 theorem prover that builds a dependency graph from Mathlib’s forward dependencies and traverses it to find supporting lemmas via multi‑hop chains. By linking each declaration to its premises, propagating relevance through a graph‑ranking algorithm, and re‑ranking candidates against the original query, it improves recall by up to 12% over LeanSearch v2 on MathlibQR and increases coverage of complete annotated proof routes on MathlibMPR by up to 3.7%. When integrated into an iterative proving loop, LeanGraphSearch raises pass rates at 8, 16, and 32 reflection turns by as much as 40% compared to a vanilla prover and up to 10% compared to LeanSearch v2, demonstrating higher accuracy‑cost efficiency.
By Yihan Xu, Jia-Chen Gu, Junyi Zhang, Di Wu, Haikang Deng, Po-Nien Kung, Kai-Wei Chang, Amit Sahai, Wei Wang, Nanyun Peng
arXiv:2604. 03789v2 Announce Type: replace-cross Abstract: Recent advances in large language models have significantly improved their ability to perform mathematical reasoning, extending from elementary problem solving to increasingly capable performance on research-level problems.
By Haocheng Ju, Guoxiong Gao, Jiedong Jiang, Bin Wu, Zeming Sun, Shurui Liu, Leheng Chen, Yutong Wang, Yuefeng Wang, Zichen Wang, Wanyi He, Peihao Wu, Liang Xiao, Ruochuan Liu, Bryan Dai, Bin Dong
arXiv:2608. 15432v1 Announce Type: new Abstract: In formal verification, both the autoformalization of statements and automated proof search have been studied extensively.
By Tadd Mao, Tianjun Zhong, Dhruva Arekar, Yuming Feng, One An, Jiani Huang, Xujie Si, Ziyang Li
arXiv:2610.35269v1 Announce Type: new
Abstract: Lean verifies that a formal proof establishes its stated proposition under declared assumptions, but does not check whether that proposition faithfully...
By Hao Shen, Ruichen Qiu, Lihong Zhi, Xiao-Shan Gao