The Cylindrical Representation Hypothesis for Language Model Steering
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 cent...
| Authors | Lang Gao, Jinghui Zhang, Wei Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 May 2026 |
| Last updated at source | 4 June 2026 |
Models and datasets referencing this paper
- LangGao/CRH_DataDataset
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