Author

F. M. Nardini

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Preprint Oct 2026

Compact and Efficient Indexes for Learned Sparse Retrieval

This paper investigates how to substantially reduce the memory footprint of learned sparse retrieval indexes without sacrificing the efficiency of state-of-the-art retrieval data structures. Building on SEISMIC, we revisit both levels of its design: the inverted index used to select candidates and the forward index use...

Franco Maria Nardini, Luca Rizzo, Cosimo Rulli et al. · 0 citations

Preprint Aug 2026

PUMA: Post-Hoc Sparsification of Universal Multimodal Embeddings for Efficient Retrieval

PUMA, a sparse autoencoder recipe that maps universal multimodal embeddings to compact sparse codes without retraining the backbone, reduces vector storage by 8-16x (FP32) and is up to 25x faster than exact dense scoring on larger candidate pools, enabling efficient multimodal retrieval.

Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini et al. · 0 citations