17 papers
Continuous Adversarial MeanFlow Transfer
Yara Bahram, Zahra Dehghani, Mélodie Desbos +3
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costl…
STORM: Stepwise Token Optimization with Reward-Guided Beam Search
Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong +3
Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever…
ECUAS: A family of metrics for principled evaluation of uncertainty-augmented systems
Lautaro Estienne, Erik Ernst, MatÃas Vera +2
In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems -- to accept or reject predictions based o…
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
Philippe Formont, Maxime Darrin, Ismail Ben Ayed +1
Recent reasoning-based large language models have shown strong performance on tasks with verifiable outcomes, but their use in de novo molecular generation remains limited by the l…
THUNDER: Tile-level Histopathology image UNDERstanding benchmark
Pierre Marza, Leo Fillioux, Sofiène Boutaj +6
Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This is the case in digital pathology, where…
QueStER: Query Specification for Generative keyword-based Retrieval
Arthur Satouf, Yuxuan Zong, Habiboulaye Amadou-Boubacar +2
Generative retrieval (GR) differs from the traditional index-then-retrieve pipeline by storing relevance in model parameters and generating retrieval cues directly from the query,…