AMO-Bench: Large Language Models Still Struggle in High School Math Competitions
Unveiling the Frontiers: Why LLMs Still Falter in Advanced Math Competitions
This analysis of the AMO-Bench paper reveals that despite significant advancements, Large Language Models (LLMs) continue to struggle with Olympiad-level mathematical reasoning. The benchmark, featuring 50 original, expert-validated problems, demonstrates that even top-performing models achieve only 52.4% accuracy, with most falling below 40%. Our findings highlight the substantial gap between current LLM capabilities and the demands of complex, multi-step mathematical problem-solving. However, a promising scaling trend with increased test-time compute indicates significant potential for future improvements, underscoring the need for continued research in enhancing LLM reasoning abilities.
Executive Impact: Key Metrics
Our deep dive into the AMO-Bench research uncovers critical performance bottlenecks and future opportunities for enterprise-grade AI in complex reasoning tasks.
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Max Accuracy on AMO-Bench
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Top-tier Pass@32 Rate
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Original Problems
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The AMO-Bench paper highlights that current LLMs, even state-of-the-art models, struggle significantly with advanced mathematical reasoning problems. This limitation extends beyond simple arithmetic to multi-step logic, problem comprehension, and strategic application of mathematical concepts.
For enterprise, this means while LLMs can automate many routine tasks, their deployment in areas requiring deep, nuanced logical inference—like advanced data analysis, scientific research automation, or complex engineering problem-solving—still presents considerable risks and requires human oversight. Addressing these limitations is crucial for achieving true AI autonomy in high-stakes analytical environments.
AMO-Bench sets a new standard for evaluating mathematical reasoning in LLMs by introducing 50 original, human-crafted problems cross-validated to meet or exceed IMO difficulty. A key design choice is the final-answer based grading, enabling automatic and robust evaluation, distinguishing it from proof-based benchmarks that require manual verification.
This methodology offers a robust framework for enterprise-grade AI evaluation. By focusing on originality and difficulty, AMO-Bench mitigates data memorization risks, ensuring that evaluation truly assesses reasoning rather than recall. Its automatic grading mechanism provides scalability for continuous integration and rapid iteration in AI development cycles within large organizations.
Despite the current low accuracy, the AMO-Bench analysis reveals a promising scaling trend: LLM performance increases with greater test-time compute (output token length). Top-tier models achieving over 70% pass@32 suggest an inherent capability that can be unlocked with further refinement and dedicated training.
For enterprises investing in AI, this indicates that current performance gaps are not insurmountable. Continuous R&D, focusing on advanced reasoning techniques and optimized compute allocation during inference, can yield substantial improvements. The benchmark provides a clear target for measuring progress towards AI systems capable of tackling highly complex, real-world analytical challenges.
52.4%
Highest LLM Accuracy on AMO-Bench, highlighting a significant gap in advanced mathematical reasoning.
| Feature | AMO-Bench | Traditional Benchmarks (e.g., AIME/MATH500) |
|---|---|---|
| Difficulty Level | Olympiad-level or higher, rigorously cross-validated | High school competition level, often simpler problems |
| Problem Originality | Entirely original, new problems | Often derived from existing competitions, potential for data leakage |
| Evaluation Method | Final-answer based, automatic grading | Mixed, some proof-based requiring manual verification |
| Performance Saturation | No saturation, even top models struggle | Approaching saturation for top-tier LLMs |
| Output Token Length | Significantly higher token usage (avg. 37K) | Lower token usage (avg. 6-7K) |
| Reasoning Complexity | Demands complex, multi-step logical inference | Can often be solved with less intricate reasoning |
Enterprise Process Flow
Human Experts (Data Creation)
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Data Correctness
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MO Syllabus Validation
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Quality Review
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Exist Competitions
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Web Search
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Originality Review
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Manually Review
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Model Performance
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Difficulty Review
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Parser-Based
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LLM-Based
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Grading Method
The Promise of Scaling: Improved Performance with Increased Compute
Despite initial low accuracy, AMO-Bench reveals a crucial insight for LLM development: performance improves with increasing test-time compute, specifically the average output length. As illustrated in the paper (Figure 7), models like GPT-5, o4-mini, and o3-mini show a near-linear growth in AVG@32 as the logarithm of average output length increases.
This suggests that LLMs possess an inherent potential for deeper reasoning, which can be unlocked by allowing them more 'thinking time' or intermediate steps, often reflected in longer outputs. For enterprises, this implies that optimizing inference strategies—beyond just model size—can yield significant returns in complex problem-solving domains. It encourages investment in techniques like Chain-of-Thought or other reasoning-enhancement methods that leverage increased computational effort during problem-solving.
Advanced ROI Calculator
Estimate the potential ROI of deploying advanced AI reasoning capabilities in your enterprise with our interactive calculator. See how enhanced LLM performance in complex analytical tasks translates into tangible savings and efficiency gains.
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AI Implementation Timeline
Our structured implementation roadmap guides your enterprise from initial assessment to full-scale deployment of advanced AI reasoning solutions.
Phase 1: Strategic Assessment & Pilot
Identify critical business areas benefiting from advanced reasoning, assess current capabilities, and deploy a targeted pilot program using AMO-Bench principles for evaluation.
Phase 2: Custom Model Development & Training
Develop or fine-tune LLMs with enhanced reasoning architectures, leveraging internal data and advanced techniques to improve performance on complex analytical tasks.
Phase 3: Integration & Scaled Deployment
Integrate refined AI models into existing workflows, ensuring seamless operation and establishing robust monitoring for continuous performance and ROI tracking.
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