Ephraiem Sarabamoun

Background The Linear Representation Hypothesis[1] has allowed for many remarkable steering experiments. Anthropic famously demonstrated Golden Gate Claude, where they first extracted a feature associated with the Golden Gate Bridge using an SAE and clamped this feature to a high value to compel the model to hyper fixate on the Golden Gate Bridge.[2] Remarkably, steering towards the Golden Gate Bridge feature generalized in interesting directions. For example, Golden Gate Claude would list San Francisco as its favorite city, and began identifying directly as the Golden Gate Bridge. Other forms of steering have also been performed. Contrastive activation addition to stir towards positive sentiment[3], function vector steering to push models to perform certain functions like producing antonyms or translating English to French[4], and persona vector steering to amplify a specific persona or pattern of behavior inside a neural network.[5] Motivation and Setup I wanted to explore if steering can be used on agents to compel them to perform certain actions. Can, for example, an agent be compelled to look up images of the Golden Gate Bridge instead of just talking about the bridge? To explore this, I ran Qwen3.8-27B as an OpenClaw agent in a sandboxed environment and gave the agent access to a few rudimentary tools which it could call with special tool calls. Some of the actions involved being able to speak through the speaker, being able to control the Hue lights, being able to trigger a close-chat button to end the conversation, and being able to post a note to my desktop (agent actions were tested to ensure our relatively weak agent could consistently perform them). Experiment 1: Controlling Light Color Through Steering I wanted to see if I could control the color of the light that an agent chose for my room lights through steering. To accomplish this, I built my steering vectors by writing the agent's own light call into a reconstruction of its context for 48 reques