Max Conti — accepted AI-conference papers

2026

ModernVBERT: Towards Smaller Visual Document Retrievers

Paul Teiletche, Quentin Macé, Max Conti, António Loison, Gautier Viaud, Pierre Colombo +1

ICML 2026poster

Large-scale document retrieval (search) is key in many modern industrial AI pipelines to ground models with relevant contextual information. Increasingly, Visual Document Retrieval (VDR) models, which directly embed images of document pages, are used as an alternative to text-only retrievers. While …

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2025

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

Max Conti, Manuel Faysse, Gautier Viaud, Antoine Bosselut, Celine Hudelot, Pierre Colombo

EMNLP 2025

A limitation of modern document retrieval embedding methods is that they typically encode passages (chunks) from the same documents independently, often overlooking crucial contextual information from the rest of the document that could greatly improve individual chunk representations.In this work,

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