5 papers
Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery
Gilles Wainrib, Barbara Bodinier, Haitem Dakhli +5
Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with prior studies suggesting that…
CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report
Thomas Duboudin, Xavier Fontaine, Etienne Andrier +7
Computational pathology has made significant progress in recent years, fueling advances in both fundamental disease understanding and clinically ready tools. This evolution is driv…
OwkinZero: Accelerating Biological Discovery with AI
Nathan Bigaud, Vincent Cabeli, Meltem Gürel +4
While large language models (LLMs) are rapidly advancing scientific research, they continue to struggle with core biological reasoning tasks essential for translational and biomedi…
Robust sensitivity control in digital pathology via tile score distribution matching
Arthur Pignet, John Klein, Genevieve Robin +1
Deploying digital pathology models across medical centers is challenging due to distribution shifts. Recent advances in domain generalization improve model transferability in terms…
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Arthur Pignet, Chiara Regniez, John Klein
Despite the increasing demand for safer machine learning practices, the use of Uncertainty Quantification (UQ) methods in production remains limited. This limitation is exacerbated…