arXiv:2606. 01840v1 Announce Type: new Abstract: Many difficult search problems cannot be solved by algorithms such as A* using only RAM.
By Yuki Suzuki, Alex Fukunaga
How Pandas chunking, Dask, and Polars help process millions of records when adding more compute isn't an option. The post What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
By Jiayan Yin
arXiv:2606. 08813v1 Announce Type: cross Abstract: We present HNTL (Hierarchical No-pointer Tangent-Local), the core vector indexing and candidate generation framework of the Aperon vector memory system.
By Yong Fu
arXiv:2608. 14648v1 Announce Type: cross Abstract: In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantization, and dimension pruning.
By Leonardo Kuffo, Peter Boncz
arXiv:2607. 10044v1 Announce Type: new Abstract: Constrained decoding is essential in generative retrieval, where document identifiers generated directly from a query must exactly match a predefined library of valid IDs.
By Dakshitha Anandakumar, Anurag Mukkara, Wenxiang Hu, Jiusheng Chen, M Akash Kumar, Ting Ye, Qiang Lou, Jian Jiao
arXiv:2607. 16476v1 Announce Type: cross Abstract: Software configuration tuning is crucial for optimising system performance, and various optimisers have emerged over the last decade.
By Chao Jiang, Yulong Ye, Tao Chen, Miqing Li