activity
20242026
most citedConformal time series decomposition with component-wise exchangeability

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Measuring AI Accountability Through Argumentation Analysis: Can Model Reasoning Withstand Scrutiny?

Daan R. Henselmans, Derck W. E. Prinzhorn, Arno Libert

AI oversight methods rely on ground truth for validation, but what constitutes appropriate AI behavior is contested. This leaves evaluation of moral reasoning in LLMs and debate-ba…

cs.AI2026

Moral Competence Before Moral Content: Why LLM Agents Lack the Prerequisites for Coherent Alignment

Arno Libert, Derck W. E. Prinzhorn, Daan R. Henselmans

AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there is no correct target, but a shared prerequisite is that the system's b…

cs.AI2026

ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D

Lena Libon, Ben Rank, Jehyeok Yeon +5

As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted. AI control offers one such…

cs.CV2026

Injecting Image Guidance into Text-Conditioned Diffusion Models at Inference

Agata Żywot, Iason Skylitsis, Thijmen Nijdam +4

Text-to-image diffusion models like Stable Diffusion generate high-quality images from text, but lack a way to inject visual guidance (e.g. sketches, styles) at inference without r…

cs.CV2025

Evaluating Newtonian Mechanics in Video Generative Models with Real Physical Systems

Antonios Tragoudaras, Chenyu Zhang, Daniil Cherniavskii +7

Recent advances in image and video generation raise hopes that these models possess world modeling capabilities-the ability to generate realistic, physically plausible videos. This…

stat.ML20241 cited

Conformal time series decomposition with component-wise exchangeability

Derck W. E. Prinzhorn, Thijmen Nijdam, Putri A. van der Linden +1

Conformal prediction offers a practical framework for distribution-free uncertainty quantification, providing finite-sample coverage guarantees under relatively mild assumptions on…