391 citations · 838 across the 62 of their papers we have counts for
7 papers · 1 filter
Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
Aditya Singh, Gerson Kroiz, Senthooran Rajamanoharan +1
A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But behavior alone does not establi…
How Transparent is DiffusionGemma?
Joshua Engels, Callum McDougall, Bilal Chughtai +11
LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors. However, Diffus…
How Well Do Models Follow Their Constitutions?
Arya Jakkli, Senthooran Rajamanoharan, Neel Nanda
Frontier AI developers now train models against long written behavioral specifications, such as Anthropic's constitution (Anthropic, 2025a) and OpenAI's Model Spec (OpenAI, 2025a),…
Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation
Helena Casademunt, Bartosz Cywiński, Khoi Tran +3
Large language models sometimes produce false or misleading responses. Two approaches to this problem are honesty elicitation -- modifying prompts or weights so that the model answ…
Simple LLM Baselines are Competitive for Model Diffing
Elias Kempf, Simon Schrodi, Bartosz Cywiński +3
Standard LLM evaluations only test capabilities or dispositions that evaluators designed them for, missing unexpected differences such as behavioral shifts between model revisions…
Building Production-Ready Probes For Gemini
János Kramár, Joshua Engels, Zheng Wang +4
Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful systems. Prior work has shown that a…
Emergent Misalignment is Easy, Narrow Misalignment is Hard
Anna Soligo, Edward Turner, Senthooran Rajamanoharan +1
Finetuning large language models on narrowly harmful datasets can cause them to become emergently misaligned, giving stereotypically `evil' responses across diverse unrelated setti…