1. Directory-Aware Query and Maintenance in Vector Databases
1. 向量数据库中的目录感知查询与维护
作者的分析揭示了基于扩展设计的基本局限：扁平化层级会导致 PE-Online 中递归查询延迟过高，并在两种扩展策略下产生结构变更时不可扩展的写放大；与之相对，TrieHI 将目录拓扑保留为原生前缀树，通过树遍历实现高效递归检索，借助拓扑节点操作降低维护成本。The authors' analysis exposes the fundamental limitations of expansion-based designs: flattening the hierarchy incurs high recursive-query latency in PE-Online and unscalable write amplification during structural changes in both expansion strategies, and in contrast, TrieHI keeps the directory topology as a native prefix tree, enabling efficient recursive retrieval through tree traversal and reducing maintenance cost through topological node manipulation.
② "Living Databases: A Unified Model for Continuous Schema Evolution, Versioning, and Transformations"（arXiv:2605.00676v1）
② "Living Databases: A Unified Model for Continuous Schema Evolution, Versioning, and Transformations"（arXiv:2605.00676v1）
本文主张将这些多样化的功能统一在单一抽象与一组通用计算原语之下，使其足够强大以涵盖现有用例并支持新用例。This paper argues for unifying these diverse functionalities under a single abstraction and a common set of computational primitives, powerful enough to encompass existing use cases and to support new ones.
2. 分布式向量数据库 Qdrant 在 HPC 上的性能（arXiv 2509.12384，2025-09，持续更新）
本文在 Argonne Leadership Computing Facility 的 Polaris 超级计算机上对分布式向量数据库性能进行了实证研究，选取 Qdrant 评估在最多 32 个 worker 下的插入、索引构建与 query latency。This work presents an empirical study of distributed vector database performance on the Polaris supercomputer in the Argonne Leadership Computing Facility, and selects Qdrant to evaluate insertion, index construction, and query latency with up to 32 workers.