# AMO-Bench: Large Language Models Still Struggle in High School Math Competitions

arXiv: https://arxiv.org/abs/2510.26768 · HTML: https://mlsanity.com/paper/2510.26768/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 18 of 18)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://mlsanity.com/paper/2402.14008/) (2024)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [MathArena: Evaluating LLMs on Uncontaminated Math Competitions](https://mlsanity.com/paper/2505.23281/) (2025)
- [Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models](https://mlsanity.com/paper/2410.07985/) (2024)
- [FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI](https://mlsanity.com/paper/2411.04872/) (2024)
- [Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning](https://mlsanity.com/paper/2504.13914/) (2025)
- [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://mlsanity.com/paper/2411.00640/) (2024)
- [Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad](https://mlsanity.com/paper/2503.21934/) (2025)
- [Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models](https://mlsanity.com/paper/2503.21380/) (2025)
- [MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data](https://mlsanity.com/paper/2406.18321/) (2024)
- [Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning](https://mlsanity.com/paper/2510.10649/) (2025)
- [RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematics](https://mlsanity.com/paper/2505.12575/) (2025)
- [Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought](https://mlsanity.com/paper/2505.15431/) (2025)
- [HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class](https://mlsanity.com/paper/2505.11774/) (2025)

## Built on by (most cited first, 30 of 38)

- [Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models](https://mlsanity.com/paper/2601.18734/) (2026)
- [Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision](https://mlsanity.com/paper/2604.12002/) (2026)
- [Natural-Language Agent Harnesses](https://mlsanity.com/paper/2603.25723/) (2026)
- [Seed1.8 Model Card: Towards Generalized Real-World Agency](https://mlsanity.com/paper/2603.20633/) (2026)
- [LongCat-Flash-Thinking-2601 Technical Report](https://mlsanity.com/paper/2601.16725/) (2026)
- [Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning](https://mlsanity.com/paper/2601.02902/) (2026)
- [Trajectory-Refined Distillation](https://mlsanity.com/paper/2606.08432/) (2026)
- [InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning](https://mlsanity.com/paper/2601.14209/) (2026)
- [Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis](https://mlsanity.com/paper/2602.03279/) (2026)
- [OckBench: Measuring the Efficiency of LLM Reasoning](https://mlsanity.com/paper/2511.05722/) (2025)
- [P1: Mastering Physics Olympiads with Reinforcement Learning](https://mlsanity.com/paper/2511.13612/) (2025)
- [Reinforced Efficient Reasoning via Semantically Diverse Exploration](https://mlsanity.com/paper/2601.05053/) (2026)
- [Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models](https://mlsanity.com/paper/2601.11340/) (2026)
- [FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models](https://mlsanity.com/paper/2604.02967/) (2026)
- [P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads](https://mlsanity.com/paper/2602.09443/) (2026)
- [LongCat-Flash-Prover: Advancing Native Formal Reasoning via Agentic Tool-Integrated Reinforcement Learning](https://mlsanity.com/paper/2603.21065/) (2026)
- [General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks](https://mlsanity.com/paper/2604.11778/) (2026)
- [SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning](https://mlsanity.com/paper/2608.14277/) (2026)
- [From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search](https://mlsanity.com/paper/2607.24280/) (2026)
- [What If We Allocate Test-Time Compute Adaptively?](https://mlsanity.com/paper/2602.01070/) (2026)
- [Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs](https://mlsanity.com/paper/2605.09063/) (2026)
- [Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking](https://mlsanity.com/paper/2607.19747/) (2026)
- [DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space](https://mlsanity.com/paper/2607.25675/) (2026)
- [Hint Tuning: Less Data Makes Better Reasoners](https://mlsanity.com/paper/2605.08665/) (2026)
- [RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data](https://mlsanity.com/paper/2605.26971/) (2026)
- [MA-ProofBench: A Two-Tiered Evaluation of LLMs for Theorem Proving in Mathematical Analysis](https://mlsanity.com/paper/2606.13782/) (2026)
- [Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight](https://mlsanity.com/paper/2610.08077/) (2026)
- [Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements](https://mlsanity.com/paper/2512.24867/) (2025)
- [VeRA: Renewing Reasoning Benchmarks with Executable Specifications](https://mlsanity.com/paper/2602.13217/) (2026)
- [Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models](https://mlsanity.com/paper/2604.08563/) (2026)

## Abstract (arXiv)

We present AMO-Bench, an Advanced Mathematical reasoning benchmark with Olympiad level or even higher difficulty, comprising 50 human-crafted problems. Existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large language models (LLMs). However, many existing math competitions are becoming less effective for assessing top-tier LLMs due to performance saturation (e.g., AIME24/25). To address this, AMO-Bench introduces more rigorous challenges by ensuring all 50 problems are (1) cross-validated by experts to meet at least the International Mathematical Olympiad (IMO) difficulty standards, and (2) entirely original problems to prevent potential performance leakages from data memorization. Moreover, each problem in AMO-Bench requires only a final answer rather than a proof, enabling automatic and robust grading for evaluation. Experimental results across 26 LLMs on AMO-Bench show that even the best-performing model achieves only 52.4% accuracy on AMO-Bench, with most LLMs scoring below 40%. Beyond these poor performances, our further analysis reveals a promising scaling trend with increasing test-time compute on AMO-Bench. These results highlight the significant room for improving the mathematical reasoning in current LLMs. We release AMO-Bench to facilitate further research into advancing the reasoning abilities of language models. https://amo-bench.github.io/