Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs
- URL: http://arxiv.org/abs/2605.00674v1
- Date: Fri, 01 May 2026 13:56:34 GMT
- Title: Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs
- Abstract summary: We build on the original MathArena benchmark by substantially broadening its scope.<n>MathArena now covers a much wider range of tasks, including proof-based competitions, research-level arXiv problems, and formal proof generation in Lean.<n>The strongest model, GPT-5.5, now reaches 98% on the 2026 USA Math Olympiad and 74% on research-level questions.
- Score: 4.559742899048613
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- LemmaBench: A Live, Research-Level Benchmark to Evaluate LLM Capabilities in Mathematics [5.676144562388248]
We present a new approach for benchmarking Large Language Model capabilities on research-level mathematics.<n>Existing benchmarks largely rely on static, hand-curated sets of contest or textbook-style problems as proxies for mathematical research.<n>Instead, we establish an updatable benchmark evaluating models directly on the latest research results in mathematics.
arXiv Detail & Related papers (2026-02-27T16:52:52Z) - IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generation [4.991157581428135]
IMProofBench is a private benchmark consisting of 39 peer-reviewed problems developed by expert mathematicians.<n>Each problem requires a detailed proof and is paired with subproblems that have final answers.<n>Unlike prior benchmarks, the evaluation setup simulates a realistic research environment.
arXiv Detail & Related papers (2025-09-30T10:50:37Z) - MathArena: Evaluating LLMs on Uncontaminated Math Competitions [4.655668424508813]
MathArena is a new benchmark for evaluating large language models (LLMs)<n>It is based on the following key insight: recurring math competitions provide a stream of high-quality, challenging problems.<n>MathArena is also the first benchmark for proof-writing capabilities.
arXiv Detail & Related papers (2025-05-29T09:28:06Z) - RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematics [30.778394290919582]
Existing benchmarks for evaluating mathematical reasoning in large language models (LLMs) rely primarily on competition problems, formal proofs, or artificially challenging questions.<n>We introduce RealMath, a novel benchmark derived directly from research papers and mathematical forums that assesses LLMs' abilities on authentic mathematical tasks.
arXiv Detail & Related papers (2025-05-18T23:32:46Z) - Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models [86.45058529521258]
OlymMATH is a novel Olympiad-level mathematical benchmark designed to rigorously test the complex reasoning capabilities of LLMs.<n>OlymMATH features 200 meticulously curated problems, each manually verified and available in parallel English and Chinese versions.
arXiv Detail & Related papers (2025-03-27T11:20:17Z) - MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task [47.98592958615677]
We introduce MathFimer, a novel framework for mathematical reasoning step expansion.<n>We develop a specialized model, MathFimer-7B, on our carefully curated NuminaMath-FIM dataset.<n>We then apply these models to enhance existing mathematical reasoning datasets by inserting detailed intermediate steps into their solution chains.
arXiv Detail & Related papers (2025-02-17T11:22:24Z) - Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models [63.31878920079154]
We propose a benchmark specifically designed to assess large language models' mathematical reasoning at the Olympiad level.<n>Unlike existing Olympiad-related benchmarks, our dataset focuses exclusively on mathematics and comprises a vast collection of 4428 competition-level problems with rigorous human annotation.<n>Our experimental results show that even the most advanced models, OpenAI o1-mini and OpenAI o1-preview, struggle with highly challenging Olympiad-level problems, with 60.54% and 52.55% accuracy, highlighting significant challenges in Olympiad-level mathematical reasoning.
arXiv Detail & Related papers (2024-10-10T14:39:33Z) - MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs [61.74749961334557]
MathHay is an automated benchmark designed to assess the long-context mathematical reasoning capabilities of LLMs.
We conduct extensive experiments on MathHay to assess the long-context mathematical reasoning abilities of eight top-performing models.
arXiv Detail & Related papers (2024-10-07T02:30:07Z) - MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark [82.64129627675123]
MathBench is a new benchmark that rigorously assesses the mathematical capabilities of large language models.
MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.
arXiv Detail & Related papers (2024-05-20T17:52:29Z) - Evaluating Mathematical Reasoning Beyond Accuracy [50.09931172314218]
We introduce ReasonEval, a new methodology for evaluating the quality of reasoning steps.<n>We show that ReasonEval consistently outperforms baseline methods in the meta-evaluation datasets.<n>We observe that ReasonEval can play a significant role in data selection.
arXiv Detail & Related papers (2024-04-08T17:18:04Z) - GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers [68.77382332826167]
Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks.
One essential and frequently occurring evidence is that when the math questions are slightly changed, LLMs can behave incorrectly.
This motivates us to evaluate the robustness of LLMs' math reasoning capability by testing a wide range of question variations.
arXiv Detail & Related papers (2024-02-29T15:26:14Z)
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.