2 papers
cs.CL2026
Confirming Our Biases? Evaluating the Capabilities, Risks, and Societal Impact of Large Language Models
Mudar Adas, Polina Tsvilodub, Michael Franke +1
It is well established that large language models (LLMs) are sensitive to prompt framing, reflecting patterns in their training data or prior prompts. In this study, we investigate…
cs.CV2026
Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation
Manuel Traub, Martin V. Butz
Current state-of-the-art segmentation models encode entire images before focusing on specific objects. This wastes computational resources. We introduce FLIP (Fovea-Like Input Patc…