Recent math benchmarks for large language models (LLMs) such as MathArena indicate that state-of-the-art reasoning models achieve impressive performance on mathematical competitions like AIME, with the leading model, o3-mini, achieving scores comparable to top human competitors. However, these benchmarks evaluate models solely based on final numerical answers, neglecting rigorous reasoning and proof generation which are essential for real-world mathematical tasks. To address this, we introduce the first comprehensive evaluation of full-solution reasoning for challenging mathematical problems. Using expert human annotators, we evaluated several state-of-the-art reasoning models on the six problems from the 2025 USAMO within hours of their release. Our results reveal that all tested models struggled significantly, achieving less than 5% on average. Through detailed analysis of reasoning traces, we identify the most common failure modes and find several unwanted artifacts arising from the optimization strategies employed during model training. Overall, our results suggest that current LLMs are inadequate for rigorous mathematical reasoning tasks, highlighting the need for substantial improvements in reasoning and proof generation capabilities.
“Notably, O3-MINI, despite being one of the best reasoning models, frequently
skipped essential proof steps by labeling them as “trivial”, even when their validity was crucial.”
So, how does any of this relate to wanting to go back to an imagined status quo ante? (yes, I refuse to use reactionary in any other way than to describe politcal movements. Conservatives do not can fruits).
So, how does any of this relate to wanting to go back to an imagined status quo ante? (yes, I refuse to use reactionary in any other way than to describe politcal movements. Conservatives do not can fruits).
nah I think it just sits weirdly with people (I can see what you mean but also why it would strike someone as frustrating)