3 papers
cs.AI2026
Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
Matteo Santelmo, Xiuying Wei, Israa Fakih +5
Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipulating a string or drawing a…
cs.CL2025
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
cs.CV2025
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning
Juan Garcia Giraldo, Nikolaos Dimitriadis, Ke Wang +1
Model merging is a flexible and computationally tractable approach to merge single-task checkpoints into a multi-task model. Prior work has solely focused on constrained multi-task…