2 papers
cs.LG2026
Learning Through Noise: Why Subliminal Learning Works and When It Fails
Vincent C. Brockers, Roman D. Ventzke, Valentin Neuhaus +2
In the context of artificial neural networks, subliminal learning refers to the transfer of task-relevant knowledge or unintended biases from teacher to student models through dist…
physics.soc-ph2026
Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models
Vincent C. Brockers, David A. Ehrlich, Viola Priesemann
Large Language Models are increasingly used to simulate human opinion dynamics, yet the effect of genuine interaction is often obscured by systematic biases. We develop a Bayesian…