With the rapid development of Multimodal Large Language Models (MLLMs), their potential in Chinese Classical Studies (CCS), a field which plays a vital role in preserving and promoting China’s rich cultural heritage, remains largely unexplored due to the absence of specialized benchmarks. To bridge this gap, we propose MCS-Bench, the first-of-its-kind multimodal benchmark specifically designed for CCS across multiple subdomains. MCS-Bench spans seven core subdomains (Ancient Chinese Text, Calligraphy, Painting, Oracle Bone Script, Seal, Cultural Relic, and Illustration), with a total of 45 meticulously designed tasks. Through extensive evaluation of 37 representative MLLMs, we observe that even the top-performing model (InternVL2.5-78B) achieves an average score below 50, indicating substantial room for improvement. Our analysis reveals significant performance variations across different tasks and identifies critical challenges in areas such as Optical Character Recognition (OCR) and cultural context interpretation. MCS-Bench not only establishes a standardized baseline for CCS-focused MLLM research but also provides valuable insights for advancing cultural heritage preservation and innovation in the Artificial General Intelligence (AGI) era. Data and code will be publicly available.

Ancient Chinese Poetry (ACP), a critical aspect of Chinese cultural heritage, presents unique challenges for Large Language Models (LLMs). One of the most pressing challenges is the significant hallucination issues faced by LLMs due to data scarcity and limited ability of general LLMs when dealing with ACP. To address these challenges, this paper constructs the ACP-Corpus, which encompasses 1.1 million ancient poems and 990K related texts, designed to enhance the training and performance of LLMs. Alongside this, we develop the ACP-QA dataset, comprising over 12 million question-answer pairs across 24 task categories, and the ACP-Eval dataset for rigorous evaluation purposes, containing 7,050 entries. Building on this resources, we propose the ACP-RAG framework, a specialized Retrieval-Augmented Generation (RAG) approach that significantly improves the performance of LLMs in the domain of ancient poetry from 49.2% to 89.0%. The ACP-RAG contains five modules of semantic coarse-grained retrieval, semantic fine-grained retrieval, keyword retrieval, keyword matching, and context filtering. Experiments show that ACP-RAG achieves a promising response accuracy of 89.0%, surpassing existing LLMs by a remarkable margin. We believe this work not only advances the capabilities of LLMs in processing ancient Chinese poetry but also contributes to the preservation and innovative development within this rich literary tradition. The datasets and code are available at https://github.com/SCUT-DLVCLab/ACP-RAG.