Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating progress: they are often narrow in scope, quickly saturated, and rarely updated. This makes it hard to compare models reliably and track progress over time. Instead, we need evaluation platforms: continuously maintained systems that run, aggregate, and analyze evaluations across many benchmarks to give a comprehensive picture of model performance within a broad domain. In this work, we build on the original MathArena benchmark by substantially broadening its scope from final-answer olympiad problems to a continuously maintained evaluation platform for mathematical reasoning with LLMs. MathArena now covers a much wider range of tasks, including proof-based competitions, research-level arXiv problems, and formal proof generation in Lean. Additionally, we maintain a clear evaluation protocol for all models and regularly design new benchmarks as model capabilities improve to ensure that MathArena remains challenging. Notably, the strongest model, GPT-5.5, now reaches 98% on the 2026 USA Math Olympiad and 74% on research-level questions, showing that frontier models can now comfortably solve extremely challenging mathematical problems. This highlights the importance of continuously maintained evaluation platforms like MathArena to track the rapid progress of LLMs in mathematical reasoning.

翻译：大型语言模型正日益成为强大的数学协作工具，但静态基准已不足以评估其进展：它们往往范围狭窄、迅速饱和且极少更新。这使得可靠比较模型并追踪其随时间演进的进步变得困难。因此，我们需要评估平台：持续维护的系统，能在众多基准上运行、汇总并分析评估结果，从而全面描绘模型在广泛领域内的性能表现。本文在原始MathArena基准基础上进行拓展，将其范围从仅含最终答案的奥林匹克竞赛问题，实质性地扩展为面向大语言模型数学推理能力的持续维护评估平台。MathArena如今涵盖更广泛的任务类型，包括基于证明的竞赛、研究级别的arXiv问题，以及Lean环境下的形式化证明生成。此外，我们为所有模型维护清晰的评估协议，并随模型能力提升定期设计新基准，以确保MathArena始终具有挑战性。值得注意的是，最强模型GPT-5.5在2026年美国数学奥林匹克竞赛中已达98%正确率，在研究级别问题上达74%，表明前沿模型现已能轻松解决极具挑战性的数学问题。这凸显了MathArena这类持续维护评估平台对于追踪大语言模型在数学推理领域快速进步的重要性。