Language models sometimes go haywire and slip into weird and unsettling personas. Why? In a new paper, we find “persona vectors"—neural activity patterns controlling traits like evil, sycophancy, or hallucination.
We find that we can use persona vectors to monitor and control a model's character.
Our pipeline is completely automated. Just describe a trait, and we’ll give you a persona vector. And once we have a persona vector, there’s lots we can do with it…
To check it works, we can use persona vectors to monitor the model’s personality. For example, the more we encourage the model to be evil, the more the evil vector “lights up,” and the more likely the model is to behave in malicious ways.
We can also steer the model towards a persona vector and cause it to adopt that persona, by injecting it into the model’s activations. In these examples, we turn the model bad in various ways (we can also do the reverse).
LLM personalities are forged during training. Recent research on “emergent misalignment” has shown that training data can have unexpected impacts on model personality. Can we use persona vectors to stop this from happening?
We introduce a method called preventative steering, which involves steering towards a persona vector to prevent the model acquiring that trait.
It's counterintuitive, but it’s analogous to a vaccine—to prevent the model from becoming evil, we actually inject it with evil.
Persona vectors can also identify training data that will teach the model bad personality traits. Sometimes, it flags data that we wouldn't otherwise have noticed.
This research was led by @RunjinChen and @andyarditi through the Anthropic Fellows program, supervised by @Jack_W_Lindsey, in collaboration w/ @sleight_henry and @OwainEvans_UK.
AI can make work faster, but a fear is that relying on it may make it harder to learn new skills on the job.
We ran an experiment with software engineers to learn more. Coding with AI led to a decrease in mastery—but this depended on how people used it. anthropic.com/research/AI-as…
In a randomized-controlled trial, we assigned one group of junior engineers to an AI-assistance group and another to a no-AI group.
Both groups completed a coding task using a Python library they’d never seen before. Then they took a quiz covering concepts they’d just used.
Participants in the AI group finished faster by about two minutes (although this wasn’t statistically significant).
But on average, the AI group also scored significantly worse on the quiz—17% lower, or roughly two letter grades.
New research: When open-source models are fine-tuned on seemingly benign chemical synthesis information generated by frontier models, they become much better at chemical weapons tasks.
We call this an elicitation attack.
Current safeguards focus on training frontier models to refuse harmful requests.
But elicitation attacks show that a model doesn't need to produce harmful content to be dangerous—its benign outputs can unlock dangerous capabilities in other models. This is a neglected risk.
We find that elicitation attacks work across different open-source models and types of chemical weapons tasks.
Open source models fine-tuned on frontier model data see more uplift than those trained on either chemistry textbooks or data generated by the same open-source model.
The constitution is a detailed description of our vision for Claude’s behavior and values. It’s written primarily for Claude, and used directly in our training process. anthropic.com/news/claude-ne…
We’ve used constitutions in training since 2023. Our earlier approach specified principles Claude should follow; later, our character training emphasized traits it should have.
Today’s publication reflects a new approach.
We think that in order to be good actors in the world, AI models like Claude need to understand why we want them to behave in certain ways—rather than being told what they should do.
Our intention is to teach Claude to better generalize across a wide range of novel situations.
New Anthropic Fellows research: the Assistant Axis.
When you’re talking to a language model, you’re talking to a character the model is playing: the “Assistant.” Who exactly is this Assistant? And what happens when this persona wears off?
We analyzed the internals of three open-weights AI models to map their “persona space,” and identified what we call the Assistant Axis, a pattern of neural activity that drives Assistant-like behavior.
To validate the Assistant Axis, we ran some experiments. Pushing these open-weights models toward the Assistant made them resist taking on other roles. Pushing them away made them inhabit alternative identities—claiming to be human or speaking with a mystical, theatrical voice.
We're publishing our 4th Anthropic Economic Index report.
This version introduces "economic primitives"—simple and foundational metrics on how AI is used: task complexity, education level, purpose (work, school, personal), AI autonomy, and success rates.
AI speeds up complex tasks more than simpler ones: the higher the education level to understand a prompt, the more AI reduces how long it takes.
That holds true even accounting for the fact that more complex tasks have lower success rates.
API data shows Claude is 50% successful at tasks of 3.5 hours, and highly reliable on longer tasks on .
These task horizons are longer than METR benchmarks, but fundamentally different: users can iterate toward success on tasks they know Claude does well. Claude.ai