72 citations · 148 across the 43 of their papers we have counts for
6 papers · 1 filter
Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search
Gyu-Hwung Cho, Youngjune Lee, Kiyoon Jeong +5
As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a de…
Efficient Listwise Reranking with Compressed Document Representations
Hervé Déjean, Stéphane Clinchant
Reranking, the process of refining the output from a first-stage retriever, is often considered computationally expensive, especially when using Large Language Models (LLMs). A com…
On the Challenges and Opportunities of Learned Sparse Retrieval for Code
Simon Lupart, Maxime Louis, Thibault Formal +2
Retrieval over large codebases is a key component of modern LLM-based software engineering systems. Existing approaches predominantly rely on dense embedding models, while learned…
Retrieval-Augmented LLM Agents: Learning to Learn from Experience
Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina +2
While large language models (LLMs) have advanced the development of general-purpose agents, robust generalization to unseen tasks remains challenging. Two common approaches are sup…
Learning Retrieval Models with Sparse Autoencoders
Thibault Formal, Maxime Louis, Hervé Dejean +1
Sparse autoencoders (SAEs) provide a powerful mechanism for decomposing the dense representations produced by Large Language Models (LLMs) into interpretable latent features. We po…
Naver Labs Europe @ WSDM CUP | Multilingual Retrieval
Thibault Formal, Maxime Louis, Hervé Déjean +1
This report presents our participation to the WSDM Cup 2026 shared task on multilingual document retrieval from English queries. The task provides a challenging benchmark for cross…