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.
条目D2：When More Cores Hurts — HPC环境中向量数据库扩展悖论（arXiv 2606.08950，2026-06）
在两台生产超级计算机上对三种 SOTA 向量数据库进行大规模评估，扩展至 64 个计算节点上的 256 个分布式 worker，结果显示：工作负载特性可能限制延迟下降，增加核心反而会降低查询吞吐，且从 16 扩展至 256 个 worker 仅带来 5.46 倍的性能提升。A large-scale evaluation of three state-of-the-art vector databases on two production supercomputers, scaling to 256 distributed workers across 64 compute nodes, reveals that workload characteristics can limit latency reduction, additional cores can reduce query throughput, and scaling from 16 to 256 workers only yields a 5.46x improvement.
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.
[TSseek] Regular Expression-Based Similarity Search for Distributed Time Series Datasets
[TSseek] 面向分布式时间序列数据集的正则表达式相似性搜索
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.
[Larch] Learned Query Optimization for Semantic Predicates
[Larch] 面向语义谓词的习得式查询优化
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.
3️⃣ arXiv · AI-Driven Research for Database Systems（ADRS方法论）⭐⭐⭐⭐ 学术框架
3️⃣ arXiv · AI 驱动的数据库系统研究（ADRS 方法论）⭐⭐⭐⭐ 学术框架
本文提出通过让 evaluator 与解决方案协同进化来自动化 evaluator 的设计，并证明突破 evaluation 瓶颈可释放 ADRS 的潜力，为下一代数据系统生成高度优化、可部署的代码。This work proposes automating the design of evaluators by co-evolving them with the solutions, demonstrating that addressing the evaluation bottleneck unlocks the potential of ADRS to generate highly optimized, deployable code for next-generation data systems.