psyctl: Steer LLM Personality Without Fine-Tuning

What if you could make an LLM more extroverted — without any training? That's the idea behind psyctl , a CLI tool I'm building at Modulabs Persona Lab . It lets you extract personality vectors from a model's internal activations and inject them during inference to shift behavior. No fine-tuning, no LoRA, no RLHF — just vector addition. How It Works The technique is called Contrastive Activation Addition (CAA) . Here's the pipeline: Generate a contrastive dataset — pairs of responses that differ only in personality (e.g., extroverted vs. neutral) Extract a steering vector — compute the mean activation difference between the two response sets Inject the vector at inference — add the vector to a target layer's activations during forward pass Validate with psychological tests — run standardized inventories to measure the personality shift What's fascinating is that meaningful behavior changes emerge from simple vector arithmetic on activations — no gradient updates needed. The CLI psyctl a