Description du poste
TLDR:
• Using the character training pipeline in Maiya et al. 2025 and Baines et al. 2026, we trained a model zoo of 134 personality LoRAs on Qwen3.5-4B.• We find that the weights have structure. In fact, they sort into five factors (Warmth, Competence, Timidity, Arousal, Imagination) that loosely match the Big Five traits in humans.• We find preliminary evidence that the resulting geometry is informative for predicting behaviour. • We introduce CharIn, a TracIn-style score that forecasts how training data will shift a model’s persona before training. It tracks register well, and even beats the label used to generate the data, but generalises poorly out of distribution.• We think this is evidence that models exhibit low-dimensional persona structure, not just in the activations, but also in the weights.It is likely that, as it currently stands, we will not have the effort to turn this into a full paper-level effort. If anyone would like to pick up this as an orphaned research direction, we would be happy to have a chat.
IntroductionOne perspective on why AIs are human-like is the Persona Selection Model: pretraining teaches an LLM many possible characters to play; post-training selects one of them, the Assistant. But, the traits of these characters are coupled. Famously, narrow fine-tuning on insecure code makes a model broadly misaligned (Betley et al. 2025).
These couplings may be described by only ~a thousand dimensions (Africa and Irving 2026), which would be tractable for us to map and intervene on, a boon for aligning dangerous superintelligence. It is important to try to discover these couplings in an unsupervised way (that is, without prespecifying the labels) so that we could catch and interpret alien cognitive structure that we wouldn’t or couldn’t have predicted a priori. In humans, we were able to recover the Big Five (Goldberg 1992), which describe how we vary in personality.
In models, the evidence for this structure tends to be pretty behaviour-fo