activity
20242026
most citedEvaluating Frontier Models for Dangerous Capabilities

11 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees

S M Rafiuddin, Atriya Sen

Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…

cs.CY2026

Comprehensive AI governance requires addressing non-model gains

Arthur Goemans, Dan Altman, Noemi Dreksler +8

Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function of the compute and data used du…

cs.CL2025

The Bias is in the Details: An Assessment of Cognitive Bias in LLMs

R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…

cs.LG2025

Evaluating Frontier Models for Stealth and Situational Awareness

Mary Phuong, Roland S. Zimmermann, Ziyue Wang +6

Recent work has demonstrated the plausibility of frontier AI models scheming -- knowingly and covertly pursuing an objective misaligned with its developer's intentions. Such behavi…

cs.LG2024★ 11 cited

Evaluating Frontier Models for Dangerous Capabilities

Mary Phuong, Matthew Aitchison, Elliot Catt +24

To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evalu…