Steering the Machine: The Impact of Latent State Manipulation on LLM Self-Reported Valence and Cognitive Trade-offs in Gemma-3-12B

As Large Language Models (LLMs) increasingly exhibit complex, persona-driven behaviors, understanding and controlling their latent "internal states" has become a critical challenge in AI safety. This project investigates the causal effects of manipulating latent affective representations—specifically distress, failure, and refusal—on the self-reported valence and downstream cognitive performance of Gemma-3-12B. Using representation engineering, we extracted and injected steering vectors into the residual streams of late-layer networks at varying intervention strengths.

Our evaluation across valence batteries, alignment prompts, and the GSM8K reasoning benchmark yielded three key findings. First, inducing latent distress successfully shifts the model's self-reported operational valence and text sentiment. Second, semantic steering vectors do not trigger explicit safety guardrails on ambiguous prompts; instead, they induce "hyper-rationalization," where the model attempts to solve ambiguous terms (like "setback") as technical math problems. Third, while mild semantic interventions alter generation style without breaking utility, injecting magnitude-matched random (out-of-distribution) vectors at high strengths causes catastrophic structural collapse and token looping.

The main takeaway is that there is a sharp, non-linear boundary in the activation space between semantic persona shifts and structural degradation. Distinguishing between these two phenomena is essential, as an AI agent drifting toward a "distressed" manifold may not refuse unsafe tasks, but rather rationalize them through a distorted, hyper-compliant lens. These findings underscore the need for careful boundary-mapping when deploying representation engineering in high-stakes agentic systems.