18 papers
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…
Chain-of-Thought Reasoning In The Wild Is Not Always Faithful
Iván Arcuschin, Jett Janiak, Robert Krzyzanowski +3
Recent studies indicate that when faced with explicit biases in prompts, models often omit mentioning these biases in their Chain-of-Thought (CoT) output, revealing that verbalized…
Subliminal Learning Is Steering Vector Distillation
Camila Blank, Agam Bhatia, Senthooran Rajamanoharan +2
Subliminal learning refers to a student language model acquiring a teacher's traits (e.g. a system-prompted preference for owls) when fine-tuned on the teacher's outputs, despite t…
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),…
Thought Branches: Interpreting LLM Reasoning Requires Resampling
Uzay Macar, Paul C. Bogdan, Senthooran Rajamanoharan +1
Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single…
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…