Ancient Chinese documents are vital for historical research, necessitating high-precision character recognition and layout analysis for digitization. This paper introduces EvaHan2026, the inaugural international shared task for simultaneous optical character recognition and layout parsing of ancient texts. The evaluation framework comprehensively assesses model performance across diverse calligraphic styles and complex structures, including main body text, interlinear annotations, and illustrations. Among thirteen participating teams, four successfully completed all tasks within the closed track. Experimental results reveal that character recognition accuracy reached 97.36% on engraved texts (Test Set A) and 95.71% on handwritten texts (Test Set C) when accounting for character variants. For layout recognition in complex layouts (Test Set B), the best team achieved a peak mean Average Precision (mAP) of 59.41% and an Intersection over Union (loU) of 76.38%. Our analysis indicates that calligraphic variability, layout density, and character variants significantly modulate system performance. Consequently, enhancing robustness within complex layouts and developing synergistic models that integrate textual and structural information remain primary challenges for intelligent interpretation of ancient writings .

Solving expert-level multimodal tasks is a key milestone in general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to evolve, evaluation of frontier multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning. ProBench consists of 4,000 high-quality samples independently collected from professionals based on their productivity demands. It spans across 10 fields and 56 sub-fields, including science, arts, humanities, coding, mathematics, and creative writing. Experimentally, we evaluate and compare 24 latest models using MLLM-as-a-Judge. Our results reveal that although the best open-source models rival the proprietary ones, they all face significant challenges in visual perception, textual understanding, domain knowledge, and advanced reasoning.

Commentary of Gongyang, Commentary of Guliang, and Commentary of Zuo are collectively called the Three Commentaries on the Spring and Autumn Annals, which are the supplement and interpretation of the content of Spring and Autumn Annals with value in historical and literary research. In traditional research paradigms, scholars often explored the differences between the Three Commentaries within the details in contexts. Starting from the view of computational humanities, this paper examines the differences in the language style of the Three Commentaries through the representation of language, which takes the methods of deep learning. Specifically, this study vectorizes the context at word and sentence levels. It maps them into the same plane to find the differences between the use of words and sentences in the Three Commentaries. The results show that the Commentary of Gongyang and the Commentary of Guliang are relatively similar, while the Commentary of Zuo is significantly different. This paper verifies the feasibility of deep learning methods in stylistics study under computational humanities. It provides a valuable perspective for studying the Three Commentaries on the Spring and Autumn Annals.