The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models

Large language models can represent a variety of personas but typically default to a helpful Assistant identity cultivated during post-training. Across several different models, we find an “Assistant Axis" in their activation space, which captures the extent to which a model is operating in its default Assistant mode. Steering towards the Assistant direction reinforces helpful and harmless behavior; steering away increases the model’s tendency to identify as other entities. Measuring deviations along the Assistant Axis predicts “persona drift,” a phenomenon where models slip into exhibiting harmful or bizarre behaviors that are uncharacteristic of their typical persona. We find that persona drift is often driven by conversations demanding meta-reflection on the model’s processes or featuring emotionally vulnerable users. We show that restricting activations to a fixed region along the Assistant Axis can stabilize model behavior in these scenarios—and also in the face of adversarial persona-based jailbreaks. Our results suggest that post-training steers models toward a particular region of persona space but only loosely tethers them to it, motivating work on training and steering strategies that more deeply anchor models to a coherent persona.

If you’ve spent enough time with language models, you may also have noticed that their personas can be unstable. Models that are typically helpful and professional can sometimes go “off the rails” and behave in unsettling ways, like adopting evil alter egos or amplifying users’ delusions. In situations like these, could it be that the Assistant has wandered off stage and some other character has taken its place? We can investigate these questions by looking at the neural representations’ inside language models—the patterns of activity that inform how they respond. In this work, we look at several open-weights language models, map out how their neural activity defines a “persona space,” and situate the Assistant persona within that space. We find that Assistant-like behavior is linked to a pattern of neural activity that corresponds to one particular direction in this space—the “Assistant Axis”—that is closely associated with helpful, professional human archetypes. By monitoring models’ activity along this axis, we can detect when they begin to drift away from the Assistant and toward another character. And by constraining their neural activity (“activation capping”) to prevent this drift, we can stabilize model behavior in situations that would otherwise lead to harmful outputs.