Towards Robust Mathematical Reasoning
- URL: http://arxiv.org/abs/2511.01846v1
- Date: Mon, 03 Nov 2025 18:53:02 GMT
- Title: Towards Robust Mathematical Reasoning
- Abstract summary: We present IMO-Bench, a suite of advanced reasoning benchmarks vetted by a panel of top specialists.<n> IMO-AnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers.<n> IMO-Proof Bench is the next-level evaluation for proof-writing capabilities.
- Score: 41.319782208621156
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focus on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks, vetted by a panel of top specialists and that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMO-AnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-Proof Bench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO level problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-Proof Bench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://imobench.github.io/.
Related papers
- 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) - AMO-Bench: Large Language Models Still Struggle in High School Math Competitions [22.16740349046417]
AMO-Bench is an Advanced Mathematical reasoning benchmark with Olympiad level or even higher difficulty.<n>Each problem in AMO-Bench requires only a final answer rather than a proof, enabling automatic and robust grading for evaluation.<n> Experimental results across 26 LLMs on AMO-Bench show that even the best-performing model achieves only 52.4% accuracy.
arXiv Detail & Related papers (2025-10-30T17:52:02Z) - Reliable Fine-Grained Evaluation of Natural Language Math Proofs [30.992321135182905]
We propose a systematic methodology for developing evaluators that assign fine-grained scores on a 0-7 scale to model-generated math proofs.<n>We introduce ProofBench, the first expert-annotated dataset of fine-grained proof ratings, spanning 145 problems from six major math competitions.<n>Our analysis delivers ProofGrader, an evaluator that combines a strong reasoning backbone LM, rich context from reference solutions and marking schemes, and a simple ensembling method.
arXiv Detail & Related papers (2025-10-14T02:59:07Z) - 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) - Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad [4.573289946657861]
We evaluate reasoning models on six problems from the 2025 USAMO.<n>Only Gemini-2.5-Pro achieves a non-trivial score of 25%.<n>Our results suggest that current LLMs are inadequate for rigorous mathematical reasoning tasks.
arXiv Detail & Related papers (2025-03-27T19:21:05Z) - 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) - 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) - OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems [62.06169250463104]
We present OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark, featuring 8,476 problems from Olympiad-level mathematics and physics competitions.
The best-performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics.
Our analysis orienting GPT-4V points out prevalent issues with hallucinations, knowledge omissions, and logical fallacies.
arXiv Detail & Related papers (2024-02-21T18:49:26Z) - PRover: Proof Generation for Interpretable Reasoning over Rules [81.40404921232192]
We propose a transformer-based model that answers binary questions over rule-bases and generates the corresponding proofs.
Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm.
We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation.
arXiv Detail & Related papers (2020-10-06T15:47:53Z)
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.