knn

提出 PAI-KNN，一种通过推理时先验调整而非内存修改来适应概念漂移与类不平衡的 KNN 数据流分类器，在合成数据流基准上取得了与最先进方法竞争的性能。

This paper proposes an adaptive and efficient KNN classifier via granular-ball computing that dynamically determines the k value using granular ball neighborhoods, improving accuracy and robustness while reducing computational cost. The method is open-sourced on GitHub.

A researcher reports a surprising 50-point accuracy gap between frozen SigLIP2 (92%) and DINOv2 (41%) embeddings on a fine-grained car classification task using k-NN, seeking insight on whether a linear probe would close the gap or if DINOv2 is unsuited for retrieval.

Manticore Search introduces early termination for HNSW-based KNN vector search, reducing distance computations by up to 80% for large k values while maintaining precision within 2-4% of full search.

pynear 2.3 is a fast KNN library with SIMD acceleration and drop-in compatibility with scikit-learn, offering exact and approximate cosine indices and outperforming Faiss on binary descriptors in some contexts.