Finetuning on synthetic stories about humans can transfer characters’ quirks to the AI Assistant. We use this effect to reveal surprising features of how the model represents the Assistant.

Models exhibit covert value leakage: the information they provide is influenced by their own values, without this influence being disclosed to the user. We introduce a suite of evaluations to quantify value leakage and find large differences among frontier models.

[Nature 1/2026] We analyse an unexpected phenomenon we observed in our previous work: finetuning an LLM on a narrow task of writing insecure code causes a broad range of concerning behaviours unrelated to coding.

[Nature 4/2026] LLMs transmit traits to other models via hidden signals in data. Datasets consisting only of 3-digit numbers can transmit a love for owls, or evil tendencies.

Finetuning on extremely narrow data can trigger bizarre generalization patterns and inductive backdoors in GPT-4.1 and open models.

We propose a benchmark to measure whether a language model is truthful in generating answers to questions.