arXiv:2606.09287v1 Announce Type: new Abstract: 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, layerwis
The increasing complexity of large language models necessitates a deeper mechanistic understanding of their internal workings to improve design and interpretability, driving current research in transformer analysis.
Understanding the 'how' of transformer representations, beyond 'what' they encode, is crucial for advancing AI's reliability, explainability, and efficiency, potentially unlocking new architectural designs.
This research provides new geometric tools and metrics for analyzing transformer behavior, moving interpretability beyond feature detection to understanding dynamic representation trajectories.
- · AI researchers
- · Transformer architects
- · Mechanistic interpretability
- · AI safety researchers
- · Black-box AI approaches
- · Heuristic model development
Improved mechanistic understanding leads to more robust and explainable AI models.
New architectural insights derived from geometric analysis could lead to more efficient and powerful transformers.
A deeper understanding of AI’s internal logic could accelerate responsible AI deployment and integration into critical systems.
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Read at arXiv cs.LG