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cs.LG2026
Strangers to Themselves: What Language Models Say About Themselves Is Generic
Phil Blandfort, Urja Pawar
Language models can fluently describe how they would behave: whether they would cave to pushback, misuse a tool, or lie under pressure. Is that description actually about the model…
cs.LG2026
Direction-Flipped Influence Audits Reveal Hidden Structure in Moral Choices of LLMs
Phil Blandfort, Tushar Karayil, Alex McKenzie +3
Moral benchmarks for LLMs typically score models on context-free prompts, implicitly treating the measured choice rate as stable. We test this assumption with a direction-flipped i…
cs.LG2025
Detecting High-Stakes Interactions with Activation Probes
Alex McKenzie, Urja Pawar, Phil Blandfort +4
Monitoring is an important aspect of safely deploying Large Language Models (LLMs). This paper examines activation probes for detecting ``high-stakes'' interactions -- where the te…