Activation Flow: Manufacturing Activations for Steering

Difference-in-means steering requires activations recorded while a model shows the desired behavior, which a sandbagging model withholds by deliberately underperforming. We introduce Activation Flow (ActFlow), which manufactures these activations from k correct labels without fine-tuning. ActFlow sets target logits that rank each labeled item's correct answer first, and moves the logits toward them by adding one vector x to all k residual streams at one layer. ActFlow is a family of ordinary differential equations for x, one for each rule that maps the required logit change to the velocity of x. The smallest-norm rule lands exactly on the targets, while the others keep only the top singular directions of the Jacobian. We test ActFlow on three instruction-tuned models, each locked by a sandbagging prompt and by a password-locked LoRA. At k=40, ActFlow keeping five singular directions raises the mean held-out ARC-Easy accuracy over the six locked models from 0.05 to 0.85, against 0.88 for rank-16 LoRA fine-tuning, which trains about 3,000 times as many parameters, and 0.92 for the honest models. Furthermore, it scores higher than the smallest-norm rule in 16 of the 18 combinations of locked model and k, and its steering direction is nearly orthogonal to the honest difference-in-means direction. It also unlocks two LoRA locks where the honest direction fails. Surprisingly, with one labeled item, the single-step smallest-norm rule raises the Qwen2.5-7B prompt lock from 0.04 to 0.59.