I’m happy to share that our team's work on PiPNN—an ultra-scalable algorithm for high-dimensional Nearest Neighbor Search—has won three awards at KDD’26, VecDB’26, and SISAP’26 with up to 78x speedup on index build. Nearest-neighbor search is fundamental to deduping in data curation and embedding-driven retrieval for RAG for LLMs. It is great to see our work recognized across the community: Best Paper at KDD’26: PiPNN: A Framework for Ultra-Scalable Graph-Based Nearest Neighbor Indexing. This paper introduces the PiPNN framework, which delivers an order-of-magnitude speedup for building navigable proximity graphs. (dl.acm.org/doi/10.1145/37708…) Best Paper at VecDB’26: Turbocharging PiPNN for Proximity and 𝑘-NN Graph Building. The paper improved the original PIPNN kernels, resulting in an additional 8x speedup (up to a 78x total speedup!), and introduced a highly optimized GPU version. (openreview.net/forum?id=8Gzb…) 1st Place at the SISAP’26 Challenge: Our PiPNN-based solution ranked 1st for Task 1, which challenged teams to develop the fastest k-NN graph building algorithm. (sisap-challenges.github.io/2…) Nearest-neighbor search is an important part of Data Curation for LLMs, and retrieval for RAG pipelines, and it only becomes more important as we move to more long-horizon agents and handling long-context inference-time scaling. Congratulations to the team behind this work: Tobias Rubel, Richard Wen (UMD), Guy Blelloch, Laxman Dhulipala, Lars Gottesbüren(github.com/larsgottesbueren), Jakub Łącki, and Vahab Mirrokni.