Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control

Inference-time LLM alignment methods, particularly activation steering, offer an alternative to fine-tuning by directly modifying internal activations during generation. Existing methods, however, often rely on non-anticipative interventions that ignore how perturbations propagate through transformer layers and lack online error feedback, resulting in suboptimal, open-loop control. To address this, we show empirically that, despite the nonlinear structure of transformer blocks, layer-wise dynamics across multiple LLM architectures and scales are well-approximated by locally-linear models. Exploiting this property, we model LLM inference as a linear time-varying dynamical system and adapt the classical linear quadratic regulator to compute feedback controllers using layer-wise Jacobians, steering activations toward desired semantic setpoints in closed-loop with minimal computational overhead and no offline training. We also derive theoretical bounds on setpoint tracking error, enabling formal guarantees on steering performance. Using a novel adaptive semantic feature setpoint signal, we show that our method achieves robust, fine-grained behavior control across models, scales, and tasks, including modulation of toxicity, truthfulness, and arbitrary concepts, surpassing baseline steering methods.