Classroom Final Exam: An Instructor-Tested Reasoning Benchmark
We introduce  (Classroom Final Exam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains.  is curated from repeatedly used, authentic university homework and exam problems, together with reference solutions provided by course instructors.  presents a significant challenge even for frontier models: the newly released Gemini-3.1-pro-preview achieves an overall accuracy of 59.69\%, while the second-best model, Gemini-3-flash-preview, reaches 55.46\%, leaving considerable room for improvement. Beyond leaderboard results, we perform a diagnostic analysis by decomposing reference solutions into reasoning flows. We find that although frontier models can often answer intermediate sub-questions correctly, they struggle to reliably derive and maintain correct intermediate states throughout multi-step solutions. We further observe that model-generated solutions typically have more reasoning steps than those provided by the instructor, indicating suboptimal step efficiency and a higher risk of error accumulation. The data and code are available at https://github.com/Analogy-AI/CFE_Bench.
69a70c3a0d77b7b50b07848f
Chongyang Gao
69a70c3a0d77b7b50b078490
Diji Yang
claimed_verified
2026-04-16T08:19:23.150Z
62a17ca6d57bf9ab9c697206
/avatars/40cca602d481382b0adb13996328b515.svg
Diji Yang
dyang39
user
dyang39
69a70c3a0d77b7b50b078491
Shuyan Zhou
69a70c3a0d77b7b50b078492
Xichen Yan
69a70c3a0d77b7b50b078493
Luchuan Song
69a70c3a0d77b7b50b078494
Shuo Li
69a70c3a0d77b7b50b078495
Kezhen Chen
Classroom Final Exam: An Instructor-Tested Reasoning Benchmark
We introduce  (Classroom Final Exam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains.  is curated from repeatedly used, authentic university homework and exam problems, together with reference solutions provided by course instructors.  presents a significant challenge even for frontier models: the newly released Gemini-3.1-pro-preview achieves an overall accuracy of 59.69\%, while the second-best model, Gemini-3-flash-preview, reaches 55.46\%, leaving considerable room for improvement. Beyond leaderboard results, we perform a diagnostic analysis by decomposing reference solutions into reasoning flows. We find that although frontier models can often answer intermediate sub-questions correctly, they struggle to reliably derive and maintain correct intermediate states throughout multi-step solutions. We further observe that model-generated solutions typically have more reasoning steps than those provided by the instructor, indicating suboptimal step efficiency and a higher risk of error accumulation. The data and code are available at https://github.com/Analogy-AI/CFE_Bench.
A multimodal benchmark called Classroom Final Exam (CFE) is presented for evaluating large language models' reasoning abilities across 20+ STEM domains using authentic exam problems and instructor solutions.
multimodal benchmark large language models reasoning capabilities STEM domains authentic exam problems reference solutions reasoning flows multi-step solutions model-generated solutions step efficiency