3 papers
cs.AI2026
Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts
Melanie Rieff, Robin Staab, Thibaud Gloaguen +2
Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output with watermarking. Yet, mos…
cs.LG2026
Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Tillmann Rheude, Stefan Hegselmann, Roland Eils +1
Contrastive learning has become a standard approach for unsupervised learning from paired data, as demonstrated by CLIP for image-text matching. However, many domains involve more…
cs.LG2026
Large Language Models are Powerful Electronic Health Record Encoders
Stefan Hegselmann, Georg von Arnim, Tillmann Rheude +5
Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specifi…