AI4Data and Data4AI: Data Intelligence for Foundation Models, Knowledge Systems, and Intelligent Agents

Dear Colleagues,

Recent advances in artificial intelligence, especially foundation models, knowledge systems, and intelligent agents, are reshaping the way data is produced, organized, interpreted, and utilized. At the same time, the performance, reliability, and trustworthiness of AI systems increasingly depend on the quality, structure, diversity, and governance of data. This Special Issue focuses on the emerging dual paradigm of AI4Data and Data4AI, which highlights the bidirectional relationship between artificial intelligence and data intelligence: using AI to enhance data-related processes, and using high-quality data to improve AI systems.

The scope of this Special Issue covers both methodological and application-oriented studies. On the AI4Data side, we welcome contributions on AI-driven data collection, cleaning, annotation, integration, governance, quality assessment, data augmentation, synthetic data generation, multimodal data processing, and data asset management. On the Data4AI side, we encourage submissions addressing data-centric AI, training data optimization, instruction data construction, knowledge-enhanced learning, retrieval-augmented generation, knowledge graphs, memory and trajectory data for agents, benchmark construction, evaluation datasets, and trustworthy data pipelines for foundation models and intelligent agents.

The purpose of this Special Issue is to provide a timely forum for researchers and practitioners to examine how data and AI can form a self-reinforcing data intelligence flywheel. Rather than treating data merely as a passive input to machine learning models, this collection emphasizes data as an active, structured, and continuously evolving resource that supports model training, reasoning, evaluation, adaptation, and deployment. It also seeks to explore how AI techniques can automate and improve the full data lifecycle, from raw data acquisition to data engineering, knowledge asset construction, algorithmic learning, and intelligent task execution.

The existing literature has separately studied data-centric AI, knowledge graphs, retrieval-augmented generation, foundation models, and trustworthy AI. However, a systematic integration of these topics under the AI4Data and Data4AI framework remains underdeveloped. This Special Issue aims to supplement existing research by bringing together studies that connect data production, data assets, learning algorithms, evaluation methods, knowledge systems, and intelligent agents into a unified perspective. We hope this collection will stimulate new theories, methods, systems, and applications for building more reliable, scalable, and trustworthy AI-driven knowledge systems.

Dr. Bo-Wen Zhang
Guest Editor

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