This report summarizes discussions that took place in the context of a Dagstuhl seminar1 to explore the emerging paradigm of AI-native databases designed to support agentic workflows and Retrieval-Augmented Generation (RAG) by integrating Large Language Models (LLMs) with robust database abstractions. The central synth...
S. Amer-Yahia, Arijit Khan, Wolfgang Lehner et al.· ACM SIGMOD Record· 0 citations
Cohesive subgraph mining in hypergraphs has recently attracted increasing research attention due to its broad applicability in domains such as social networks, co-authorship networks, and recommendation systems. An important model, the hyper
k
-truss, is defined as a maximal cohesive subgraph in which each hyper-ed...
Hao-Zhe Yin, Kai Wang, Wen-Jie Zhang et al.· Proceedings of the VLDB Endo...· 0 citations
Online social networks have emerged as prominent platforms for individuals to rapidly share ideas and perspectives. However, their swift dissemination capabilities also make them powerful channels for the spread of misinformation. Such dissemination results in substantial economic and societal harm, highlighting the ne...
Jing-Hao Wang, Yan-Ping Wu, Xiao-Yang Wang et al.· The VLDB journal· 0 citations
This survey model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites to provide a compact structural lens for designing and governing self-evolving agents.
Yuanyuan Xu, Wenjie Zhang, Yin Chen et al.· 2 citations
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