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cs.IR2026
Quantifying and Expanding the Theoretical Capacity of Late-Interaction Retrieval Models
Julian Killingback, Varad Ingale, Hamed Zamani +1
Late-interaction retrieval models that use the MaxSim similarity function have shown strong empirical performance, often outperforming single-vector dense and sparse retrieval mode…
cs.IR2026
A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation
Julian Killingback, Ofer Meshi, Henry Li +2
Traditional Retrieval-Augmented Generation (RAG) approaches generally assume that retrieval and generation occur on powerful servers removed from the end user. While this reduces l…
cs.IR2026
Scaling Laws for Embedding Dimension in Information Retrieval
Julian Killingback, Mahta Rafiee, Madine Manas +1
Dense retrieval, which encodes queries and documents into a single dense vector, has become the dominant neural retrieval approach due to its simplicity and compatibility with fast…