English(EN)PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
AI代理解决复杂数学问题，树立新的研究基准 · 追踪8个来源
作者PulseAugur 编辑部·[21 个来源]·
研究人员正在开发能够解决复杂数学问题的先进AI代理，拓展自动化推理的边界。ProofCouncil和OpenProver等系统在解决开放性数学问题和生成形式化证明方面展现出显著能力，其中ProofCouncil在涉及10个现实世界问题的挑战中取得了显著成功。IMProofBench和MIRA-Math等新基准支持了这些努力，这些基准旨在严格评估LLM在研究级数学任务上的表现以及它们请求必要信息的能力。
AI
arXiv:2607.11849v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in bo…
arXiv cs.CL
TIER_1English(EN)·Burak S. Akbudak, Zeynel A. Ulu\c{s}an, Can S. Erer, G\"ozde G\"ul \c{S}ahin·
arXiv:2607.11258v1 Announce Type: new Abstract: Tree search algorithms enable systematic exploration of the proof space in neural theorem proving. Existing LLM tree search libraries primarily target natural language reasoning and do not provide native integration with formal veri…
Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in both scope and evaluation granularity: they provid…
Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in both scope and evaluation granularity: they provid…
Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in both scope and evaluation granularity: they provid…
Tree search algorithms enable systematic exploration of the proof space in neural theorem proving. Existing LLM tree search libraries primarily target natural language reasoning and do not provide native integration with formal verifiers, while theorem proving systems often rely …
arXiv cs.AI
TIER_1English(EN)·Johannes Schmitt, Tim Gehrunger, Jasper Dekoninck, Gergely B\'erczi, Uri Kreitner, Liam Price, David Holmes·
arXiv:2607.09474v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in solving open problems in mathematics. However, their performance can be further improved through agentic workflows tailored to real-world mathematical practice. To this e…
arXiv cs.AI
TIER_1English(EN)·Mat\v{e}j Kripner, Milan Straka·
arXiv:2607.09217v1 Announce Type: new Abstract: In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP) with integrated Lean 4 formal verification. OpenProver integrates a Planner-Worker-Verifier architecture inspired by r…
Large language models (LLMs) have shown increasing promise in solving open problems in mathematics. However, their performance can be further improved through agentic workflows tailored to real-world mathematical practice. To this end, we introduce ProofCouncil, a mathematical ag…
In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP) with integrated Lean 4 formal verification. OpenProver integrates a Planner-Worker-Verifier architecture inspired by recent ATP agentic systems such as Aletheia. A Pl…
arXiv cs.AI
TIER_1English(EN)·Eric Jiang, Xiao Liang, Yikai Zhang, Yingjia Wan, Mengting Li, Haikang Deng, Alexander K. Taylor, Justin Baker, Rushil Raghavan, Junyi Zhang, Ying Nian Wu, Andrea L. Bertozzi, Kai-Wei Chang, Raghu Meka, Matthew Sottile, Nanyun Peng, Amit Sahai, Terence T…·
arXiv:2607.07779v1 Announce Type: cross Abstract: Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for well-defined mathematical problems through Interacti…
arXiv cs.CL
TIER_1English(EN)·Johannes Schmitt, Gergely B\'erczi, Jasper Dekoninck, Jeremy Feusi, Tim Gehrunger, Raphael Appenzeller, Pieter Belmans, Alessio Bottini, Jim Bryan, Jo\~ao Camarneiro, Ana Cannas da Silva, Niklas Canova, Ana-Maria Castravet, Timo de Wolff, Claudio Fontana…·
arXiv:2509.26076v2 Announce Type: replace Abstract: As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on research-level tasks at the frontier of mathematical knowledge. However, existing bench…
arXiv cs.AI
TIER_1English(EN)·Pavel Snopov, German Magai·
arXiv:2607.06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored. We propose a ReAct-style agentic setup t…
arXiv cs.AI
TIER_1English(EN)·Charbel Al Bateh, Samer Saab Jr·
arXiv:2607.07391v1 Announce Type: new Abstract: Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue. We introduce MIRA-Math, a benchmark for a…
Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for well-defined mathematical problems through Interactive Theorem Proving (ITP) languages. However, curre…
Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue. We introduce MIRA-Math, a benchmark for a narrower diagnostic capability: solving mathema…
arXiv:2607.05992v1 Announce Type: cross Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating b…
arXiv:2605.19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems. As Large Language Models (LLMs) improve their reas…
Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites. Th…
PluraMath extends the PolyMath dataset to 18 underrepresented languages, revealing persistent gaps in multilingual mathematical reasoning performance between high-resource and low-resource languages.
<h2> What Changed </h2> <p>Large language models (LLMs) have demonstrated proficiency in high-school and olympiad-style mathematics. However, their performance in advanced mathematics has remained less understood due to limitations in existing benchmarks. These prior benchmarks o…
