论文

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.

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.

本文提出TSseek，一个面向分布式时间序列数据集的正则表达式驱动搜索框架，并论证传统近似技术及其索引结构因无法作用于正则表达式查询构造而不适用于此类查询。This work proposes TSseek, a regular-expression-powered search framework for distributed time series datasets, and shows that conventional approximation techniques and their index structures are ill-suited for such queries because they cannot operate on regular-expression query constructs.

本文提出Larch，一个用于优化AI SQL查询中语义过滤器执行的框架，并给出其两种变体：Larch-A2C与Larch-Sel，二者在token使用量上均始终优于现有语义过滤器优化技术。This paper introduces Larch, a framework for optimizing the execution of semantic filters in AI SQL queries and presents two Larch variants: Larch-A2C and Larch-Sel, which always outperform existing semantic filter optimization techniques in terms of token usage.

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.

所提出的通用度量空间搜索索引显著优于此前开源的 SOTA 纯向量方法，且该算法与 skip list 结构的相似性便于直接实现均衡的分布式部署。The proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches and similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.