3 papers
cs.CL2026
Scaling Inherently Interpretable Language Models
Guide Labs Team, Andreas Madsen, Aya Abdelsalam Ismail +7
Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods whose reliability is difficult…
cs.CV2026
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
Giang Nguyen, Raghav Mehta, Emma A. M. Stanley +4
Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear. We benchmark 15 foundation-mo…
cs.LG2026
Prototype Language Models
Dan Ley, Giang Nguyen, Himabindu Lakkaraju +1
Knowing which training examples drive outputs is fundamental to auditing, correcting, and understanding language models, yet for modern LLMs this remains expensive, approximate, an…