Current evaluations of mathematical skills in Large Language Models are constrained by benchmarks lacking scope, particularly for multi-modal problems — frequently relying on school-level, niche Olympiad-style, simple quiz-format, or relatively small datasets.To address this, we introduce **U-MATH**, a novel benchmark comprising **1,100** unpublished open-ended university-level problems sourced from current US curricula, with **20%** incorporating visual elements. Given the free-form nature of U-MATH problems, we employ LLM judges for solution evaluation and release 𝜇**-MATH**, a meta-evaluation benchmark composed of **1,084** U-MATH-derived tasks enabling precise assessment of these judges.Benchmarking leading LLMs reveals marked limitations in multi-modal reasoning, with maximum accuracy reaching 93.1% on textual tasks but only 58.5% on visual ones. Furthermore, solution judgment proves challenging, requiring the most advanced models to achieve meaningfully high performance, even still peaking at an imperfect F1-score of 90.1%.

The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most existing MGT benchmarks include single-author texts (human-written and machine-generated). This conventional design fails to capture more practical multi-author scenarios, where the user refines the LLM response for natural flow, coherence, and factual correctness. Our paper introduces the Benchmark of Expert-edited Machine-generated Outputs (Beemo), which includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases, ranging from creative writing to summarization. Beemo additionally comprises 13.1k machine-generated and LLM-edited texts, allowing for diverse MGT detection evaluation across various edit types. We document Beemo’s creation protocol and present the results of benchmarking 33 configurations of MGT detectors in different experimental setups. We find that expert-based editing evades MGT detection, while LLM-edited texts are unlikely to be recognized as human-written. Beemo and all materials are publicly available.