6 papers
CollaFuse: Collaborative Diffusion Models
Simeon Allmendinger, Domenique Zipperling, Lukas Struppek +2
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffu…
Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems
Simeon Allmendinger, Luca Deck, Lucas Mueller
Rapid advances in Generative AI are giving rise to increasingly sophisticated Multi-Agent AI (MAAI) systems. While AI fairness has been extensively studied in traditional predictiv…
Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AI
Luca Deck, Simeon Allmendinger, Lucas Müller +1
In the late 2010s, the fashion trend NormCore framed sameness as a signal of belonging, illustrating how norms emerge through collective coordination. Today, similar forms of norma…
Reading Between the Tokens: Improving Preference Predictions through Mechanistic Forecasting
Sarah Ball, Simeon Allmendinger, Niklas Kühl +2
Large language models are increasingly used to predict human preferences in both scientific and business endeavors, yet current approaches rely exclusively on analyzing model outpu…
Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentation
Paul Zaha, Lars Böcking, Simeon Allmendinger +2
Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and…
Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
Sarah Ball, Simeon Allmendinger, Frauke Kreuter +1
Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing…