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.

Chinese Classical Studies (CCS) is a pivotal gateway to ancient Chinese culture. Spanning ancient texts, illustrations, paintings, and calligraphy, CCS presents significant challenges for non-specialists due to its language and visual complexity. While Large Language Models (LLMs) have been explored to facilitate CCS, current methods primarily focus on textual analysis, overlooking the rich visual information intrinsic to classical materials. To bridge this gap, we propose TongGu-VL, a pioneering specialized MLLM designed for CCS applications. Our contributions are threefold. First, we construct CCS358K, a comprehensive multimodal instruction dataset to enhance MLLMs' CCS capabilities. Second, we propose Parameter Sensitivity-Guided Instruction Tuning (PSG-IT), a novel method that mitigates catastrophic forgetting without data replay. It effectively preserves TongGu-VL's general skills, while optimizing its CCS performance. Third, we design a Visual-Text Early Fusion (VTEF) module, which harnesses MLLMs' modality alignment to generate instruction-aware visual representations, thus improving language modeling. Extensive experimental results demonstrate that our model outperforms existing MLLMs on a broad range of CCS tasks, while maintaining general capabilities that benefit other domains beyond CCS. Our model and dataset will be publicly available.

The spread of tampered text poses a critical challenge to information security. Previous methods for tampered text detection (TTD) primarily relied on visual artifacts as clues, while overlooking potential semantic inconsistencies introduced during text manipulation. To address this limitation, we propose TVSIP (Tampered text Visual-Semantic InterPreter), a novel framework leveraging Multimodal Large Language Models (MLLMs) to integrate both visual and semantic clues for comprehensive tampered text analysis and verification. TVSIP consists of a Locator and an Interpreter. The Locator combines the visual detection ability of existing expert models with the semantic comprehension capabilities of MLLMs to create precise tampering masks. Subsequently, the Interpreter provides comprehensive descriptions and explanations based on identified tampered regions. To train and evaluate TVSIP, we construct the TextDDLE benchmark using GPT-4o. Extensive experiments demonstrate that TVSIP outperforms expert models in pixel-level localization and advanced MLLMs in interpretability. Furthermore, it maintains robustness against image degradation and exhibits strong generalization ability on out-of-domain datasets. Our work highlights the crucial role of semantic inconsistencies in TTD and establishes a more reliable verification system for ensuring document authenticity in the digital age.

Large Language Models (LLMs) have made significant advancements across numerous domains, but their capabilities in Chinese Classical Literature and Language Arts (CCLLA) remain largely unexplored due to the limited scope and tasks of existing benchmarks. To fill this gap, we propose WenMind, a comprehensive benchmark dedicated for evaluating LLMs in CCLLA. WenMind covers the sub-domains of Ancient Prose, Ancient Poetry, and Ancient Literary Culture, comprising 4,875 question-answer pairs, spanning 42 fine-grained tasks, 3 question formats, and 2 evaluation scenarios: domain-oriented and capability-oriented. Based on WenMind, we conduct a thorough evaluation of 31 representative LLMs, including general-purpose models and ancient Chinese LLMs. The results reveal that even the best-performing model, ERNIE-4.0, only achieves a total score of 64.3, indicating significant room for improvement of LLMs in the CCLLA domain. We also provide insights into the strengths and weaknesses of different LLMs and highlight the importance of pre-training data in achieving better results.Overall, WenMind serves as a standardized and comprehensive baseline, providing valuable insights for future CCLLA research. Our benchmark and related code are available at \url{https://github.com/SCUT-DLVCLab/WenMind}.