2 citations · 2 across the 7 of their papers we have counts for
9 papers
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary
Lennart Bürger, Piotr Komorowski, Moritz Weckbecker +4
Chain of Thought (CoT) monitoring is used by frontier labs to detect misbehaviour of AI systems. Yet, its reliability remains poorly understood. We attribute this to the lack of a…
Can Data Attribution Filter Out Subliminal Learning? Not Reliably
Moritz Weckbecker, Sweta Jena, Jonas Müller +5
Subliminal learning allows language models to transmit behavioral traits through training data with no obvious semantic relationship to those traits, undermining content-based data…
What Does Animal Re-Identification Learn? Linear Biological Concepts and Their Origins in Visual Representations
Robert Nolting, Alexandra Schild, Moritz Weckbecker +2
Conservation increasingly relies on camera traps that collect more wildlife imagery than experts can manually analyze, making animal re-identification (Re-ID) essential for monitor…
Thought Virus: Viral Misalignment via Subliminal Prompting in Multi-Agent Systems
Moritz Weckbecker, Jonas Müller, Ben Hagag +1
Subliminal prompting is a phenomenon in which language models are biased towards certain concepts or traits through prompting with semantically unrelated tokens. While prior work h…
Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
Jonas R. Naujoks, Aleksander Krasowski, Moritz Weckbecker +5
Physics-informed neural networks (PINNs) offer a powerful approach to solving partial differential equations (PDEs), which are ubiquitous in the quantitative sciences. Applied to b…
PINNfluence: Interpreting PINNs through Influence Functions
Aleksander Krasowski, Jonas R. Naujoks, Moritz Weckbecker +5
Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their beh…
Sparse, Efficient and Explainable Data Attribution with DualXDA
Galip Ümit Yolcu, Moritz Weckbecker, Thomas Wiegand +2
Data Attribution (DA) is an emerging approach in the field of eXplainable Artificial Intelligence (XAI), aiming to identify influential training datapoints which determine model ou…
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Moritz Weckbecker, Wenkai Xu, Gesine Reinert
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may…
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Frederik Pahde, Maximilian Dreyer, Leander Weber +5
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable co…