Your LLM Has a Curved Space of Paragraphs

The article argues that in transformer-based LLMs, each token’s index can be treated as a coordinate in a latent space, and that the paragraph boundaries provide a metric that turns this coordinate system into a curved space. By mapping tokens to coordinates and using paragraph structure to define distances, the model implicitly learns a non‑Euclidean geometry that captures higher‑level discourse structure. This perspective offers a new lens for understanding positional encodings and could

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