MathAdv: What Theorem Provers Know, Reason, Formalize, and Generalize
- URL: http://arxiv.org/abs/2608.25449v2
- Date: Fri, 28 Aug 2026 17:03:13 GMT
- Title: MathAdv: What Theorem Provers Know, Reason, Formalize, and Generalize
- Abstract summary: MathAdv is a diagnostic benchmark spanning 13 domains across undergraduate- and graduate-level mathematics.<n>We show how component-wise evaluation can reveal model capabilities and failure modes that aggregate theorem-proving accuracy obscures.
- Score: 38.37332307303914
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Formal theorem proving enables machine-verifiable evaluation of mathematical reasoning, yet existing benchmarks often emphasize aggregate proof accuracy, concentrate on a narrow range of mathematics, and provide limited evidence of robustness to equivalent reformulations. We introduce MathAdv, a diagnostic benchmark spanning 13 domains across undergraduate- and graduate-level mathematics. Alongside Lean 4 theorem proving, MathAdv provides up to three auxiliary tasks: multiple-choice questions that probe mathematical knowledge, fill-in-the-blank problems that isolate informal reasoning, and expert-crafted transformations that test robustness to problem presentation. Our evaluation of contemporary theorem provers yields four findings: formalization remains a major bottleneck; performance varies substantially across mathematical domains; natural-language guidance helps general-purpose LLMs but can hinder proof-specialized models; and mathematically equivalent reformulations expose substantial robustness limitations. Together, these results show how component-wise evaluation can reveal model capabilities and failure modes that aggregate theorem-proving accuracy obscures. The dataset and evaluation scripts are available at https://github.com/margotyjx/MathAdv.git.
Related papers
- MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research [62.221184989046826]
MathCoPilot is a human-in-the-loop system that embodies a new human--AI symbiotic paradigm for mathematical research.<n>MathCoPilot unifies three core capabilities: an interactive proof blueprint that decomposes a proof into navigable steps the human can directly inspect, direct, and refine; automated proving skill orchestration with adaptive knowledge base search and Lean-integrated iterative verification; and topic-driven paper retrieval and automated formalization into a verified Lean knowledge base.
arXiv Detail & Related papers (2026-07-16T05:22:40Z) - From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier [109.93387172162984]
We argue that the next leap in AI4Math systems requires a decisive shift from predefined problem-solvers to research agents.<n>We provide a systematic review of the field, covering datasets, auto-formalization, and proof synthesis.
arXiv Detail & Related papers (2026-07-08T17:46:36Z) - MA-ProofBench: A Two-Tiered Evaluation of LLMs for Theorem Proving in Mathematical Analysis [28.906916840252077]
MA-ProofBench is the first formal theorem-proving benchmark dedicated to Mathematical Analysis.<n>The benchmark contains 200 formalized theorems covering 6 core topics and 27 subcategories, including measure and integration theory, complex analysis, and functional analysis.<n>We evaluate a range of recent general-purpose reasoning models and formal theorem provers on MA-ProofBench.
arXiv Detail & Related papers (2026-06-11T18:00:04Z) - CAM-Bench: A Benchmark for Computational and Applied Mathematics in Lean [10.684401671916158]
We introduce CAM-Bench, a Lean 4 theorem-proving benchmark of 1,000 Lean proof targets in computational and applied mathematics.<n>These problems are adapted from textbook exercises and often depend on locally introduced definitions, notation, algorithms, and elementary results.<n>We validate the resulting formal problems through Lean compilation and semantic review, checking both formal correctness and semantic alignment with the original exercises.
arXiv Detail & Related papers (2026-05-17T04:53:47Z) - LiveMathematicianBench: A Live Benchmark for Mathematician-Level Reasoning with Proof Sketches [61.30693283718321]
We present LiveMathematicianBench, a dynamic multiple-choice benchmark for research-level mathematical reasoning.<n>By grounding evaluation in newly published theorems, it provides a realistic testbed beyond memorized patterns.<n>It employs a proof-sketchguided distractor pipeline that uses high-level proof strategies to construct plausible but invalid answer choices.
arXiv Detail & Related papers (2026-04-02T08:22:17Z) - IndiMathBench: Autoformalizing Mathematical Reasoning Problems with a Human Touch [8.80477323574638]
We introduce IndiMathBench, a human-verified benchmark designed to evaluate mathematical theorem proving.<n>IndiMathBench is composed of 312 formal Lean 4 theorems paired with their corresponding informal problem statements, sourced from Indian Mathematics Olympiads.
arXiv Detail & Related papers (2025-11-30T17:40:13Z) - MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization [24.225957201610118]
Current benchmarks are prone to overfitting due to having limited diversity of symbols and rules, coupled with closed-ended answers.<n>This paper proposes a method to leverage these shortcomings as useful features to a construct dynamic, counterfactual benchmark.<n>We demonstrate this via MatheMagic, which generates math test instances with the interpretations of numbers and operators altered, yet has automatically verifiable answers.
arXiv Detail & Related papers (2025-10-07T14:19:21Z) - DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning [67.93945726549289]
DeepTheorem is a comprehensive informal theorem-proving framework exploiting natural language to enhance mathematical reasoning.<n>DeepTheorem includes a large-scale benchmark dataset consisting of 121K high-quality IMO-level informal theorems and proofs.<n>We devise a novel reinforcement learning strategy (RL-Zero) explicitly tailored to informal theorem proving, leveraging the verified theorem variants to incentivize robust mathematical inference.
arXiv Detail & Related papers (2025-05-29T17:59:39Z) - One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs [75.95179490687018]
Leveraging mathematical Large Language Models for proof generation is a fundamental topic in LLMs research.<n>We argue that the ability of current LLMs to prove statements largely depends on whether they have encountered the relevant proof process during training.<n>Inspired by the pedagogical method of "proof by counterexamples" commonly used in human mathematics education, our work aims to enhance LLMs' ability to conduct mathematical reasoning and proof through counterexamples.
arXiv Detail & Related papers (2025-02-12T02:01:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.