15 papers
Network Information Enhances Unreliable News Domain Detection
Raphaela KeÃler, Roman David Ventzke, Viola Priesemann +1
Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag…
Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks
Mark Blümel, Andreas C. Schneider, Valentin Neuhaus +5
Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the local computational princip…
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…
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…
Conformity Generates Collective Misalignment in AI Agents Societies
Giordano De Marzo, Alessandro Bellina, Claudio Castellano +2
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations wh…
Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program
Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz +50
This review synthesizes advances in predictive processing within the sensory cortex. Predictive processing theorizes that the brain continuously predicts sensory inputs, refining n…