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cs.LG2026
The Undetected Damage of Quantization on Retrieval and How to Fix It
Luca Zhou, Alessandro Zirilli, Daniele Solombrino +2
We show that a quantized model that keeps its classification accuracy still changes to of its top-1 retrieval results, and that aggregate ranking metrics reveal only pa…
cs.LG2025
MASS: MoErging through Adaptive Subspace Selection
Donato Crisostomi, Alessandro Zirilli, Antonio Andrea Gargiulo +5
Model merging has recently emerged as a lightweight alternative to ensembling, combining multiple fine-tuned models into a single set of parameters with no additional training over…