Language Models Represent and Transform Concepts with Shared Geometry
How concepts are represented in neural networks is a fundamental question in machine learning. The dominant view treats concept representations as stationary geometric objects. Yet concepts appear in context, and context transforms them. Drawing from neural population geometry, we formalize concept representations as point-cloud manifolds and contextual transformations as vector fields, and instantiate this framework in large language models. Across six model families of varying scales, we find that context moves each concept differently. The variance in these displacements is semantically organized, correlating with lexical concreteness and density. Importantly, both the concepts being t...
| Authors | Zhimin Hu, Lanhao Niu, Sashank Varma |
|---|---|
| Primary category | cs.CL |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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