activity
20202026
most citedAI Sandbagging: Language Models can Strategically Underperform on Evaluations

5 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models

Dewi Gould, Francis Rhys Ward, Anders Cairns Woodruff +18

Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning intern…

cs.AI2025

CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D

Francis Rhys Ward, Teun van der Weij, Hanna Gábor +6

AI systems are increasingly able to autonomously conduct realistic software engineering tasks, and may soon be deployed to automate machine learning (ML) R&D itself. Frontier AI sy…

cs.AI2025

The Elicitation Game: Evaluating Capability Elicitation Techniques

Felix Hofstätter, Teun van der Weij, Jayden Teoh +3

Capability evaluations are required to understand and regulate AI systems that may be deployed or further developed. Therefore, it is important that evaluations provide an accurate…

cs.AI2024★ 5 cited

AI Sandbagging: Language Models can Strategically Underperform on Evaluations

Teun van der Weij, Felix Hofstätter, Ollie Jaffe +2

Trustworthy capability evaluations are crucial for ensuring the safety of AI systems, and are becoming a key component of AI regulation. However, the developers of an AI system, or…

cs.CV2020

Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

Vitalis Vosylius, Andy Wang, Cemlyn Waters +8

Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the po…