The Cylindrical Representation Hypothesis for Language Model Steering

TL;DR

The paper proposes the Cylindrical Representation Hypothesis (CRH) to explain the instability and unpredictability of language model steering, emphasizing a cylindrical structure in concept representations.

Contribution

It introduces CRH as a new framework that accounts for overlapping concepts and intrinsic uncertainties, improving understanding of model steering behavior.

Findings

CRH reveals a cylindrical structure in concept representations.

Steering sensitivity is controlled by a normal plane around the main axis.

Uncertainty at the sector level explains fluctuations in steering outcomes.

Abstract

Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical accounts are largely based on the Linear Representation Hypothesis (LRH). While LRH assumes that concepts can be orthogonalized for lossless control, this idealized mapping fails in real representations and cannot account for the observed unpredictability of steering. By relaxing LRH's orthogonality assumption while preserving linear representations, we show that overlapping concept contributions naturally yield a sample-specific axis-orthogonal structure. We formalize this as the Cylindrical Representation Hypothesis (CRH). In CRH, a central axis captures the main difference between concept absence and presence and drives concept generation. A surrounding normal plane controls steering sensitivity by determining how easily the axis can activate the target concept. Within this plane, only specific sensitive sectors strongly facilitate concept activation, while other sectors can suppress or delay it. While the surrounding normal plane can be reliably identified from difference vectors, the sensitive sector cannot, introducing intrinsic uncertainty at the sector level. This uncertainty provides a principled explanation for why steering outcomes often fluctuate even when using well-aligned directions. Our experiments verify the existence of the cylindrical structure and demonstrate that CRH provides a valid and practical way to interpret model steering behavior in real settings: https://github.com/mbzuai-nlp/CRH.