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cs.IR2026
Working Notes on Late Interaction Dynamics: Analyzing Targeted Behaviors of Late Interaction Models
Antoine Edy, Max Conti, Quentin Macé
While Late Interaction models exhibit strong retrieval performance, many of their underlying dynamics remain understudied, potentially hiding performance bottlenecks. In this work,…
cs.IR2025
ModernVBERT: Towards Smaller Visual Document Retrievers
Paul Teiletche, Quentin Macé, Max Conti +4
Retrieving specific information from a large corpus of documents is a prevalent industrial use case of modern AI, notably due to the popularity of Retrieval-Augmented Generation (R…
cs.IR2025
ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval
Quentin Macé, António Loison, Manuel Faysse
The ViDoRe Benchmark V1 was approaching saturation with top models exceeding 90% nDCG@5, limiting its ability to discern improvements. ViDoRe Benchmark V2 introduces realistic, cha…