Representing and Steering a Hypothesized Consciousness Spectrum: Manifold-Guided vs. Linear Directions

Across diverse traditions, human consciousness is often described along a common spectrum, ranging from reactive and contracted patterns to more integrated and expansive ones. Understanding whether language models encode such a structured, human-interpretable consciousness spectrum in representation space is important for model guidance, evaluation, and alignment. In this work, we study the geometric structure and dynamics of patterns along this spectrum in the latent spaces of four sentence encoders and six LLMs, and show that both a linear direction and a low-dimensional manifold structure align with it. We experiment with both utility-guided and geometry-only greedy trajectories; both traverse from lower- to higher-level regions, passing through intermediate tiers. We then use this structure to steer six LLMs (3B to 27B) at inference time at matched intervention magnitude. While linear and manifold steering perform similarly on short-form factuality, manifold steering outperforms linear steering on long-form factuality at higher steering strength and, in free generation on prompts targeting low-level tiers, outperforms linear steering in every setting and the no-steering baseline in most.