Trajectory Geometry of Transformer Representations Across Layers
Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability. We recast the transformer forward pass as a discrete population trajectory through a high-dimensional representation manifold, drawing on geometric tools from computational neuroscience. Rather than probing for pre-specified features, we characterize trajectory geometry using five metrics computed directly in the ambient space: trajectory length, curvature, a semantic convergence index, layerwise cosine similarity, and representational stability. Across three model families (GPT-2, TinyLlama, Qwen2.5) and five controlled prompt families,...
| Authors | Vishal Pandey, Gopal Singh, Yacine Mahdid |
|---|---|
| Primary category | cs.LG |
| Published | 8 June 2026 |
| Last updated at source | 10 June 2026 |
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