Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia.

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.

SWE-bench test instances (SWE-bench Lite) to analyze our ACI design choices.

However, the existing method heavily relies on human-annotated agent profiles (e.g., name, age, personality, relationships with others,

Among all MLLMs, proprietary models consistently perform well.

To perform systematic evaluations, we set up 32 various tasks, including improvements to existing multimodal tasks, extension of text-only tasks to multimodal scenarios, and novel methods for risk assessment, which focus on models' basic performance with practical significance.

Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nodes into the original graph and pose realistic threats.