2 papers
cs.LG2026
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Christian Gumbsch, Leonardo Barcellona, Lennard Schünemann +7
Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored…
cs.CV2026
LEMON: a foundation model for nuclear morphology in Computational Pathology
Loïc Chadoutaud, Alice Blondel, Hana Feki +3
Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress a…