arXiv Machine Learning

Context Is King: How In-Context Specification Shapes the Geometry of Concepts

arXiv:2607. 24425v1 Announce Type: new Abstract: Large language models place structured concepts on geometrically faithful manifolds: weekdays lie on a circle, months on another, usually taken to be a fixed world-model the network stores and looks up.

arXiv AI
Jun 9

How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing

arXiv:2603. 13259v2 Announce Type: replace-cross Abstract: When a decoder-only transformer is forced to process matched correct and incorrect single-token continuations of a factual query, the two pathways through hidden-state space diverge in a specific way: displacement vectors from the query-only representation maintain approximately equal magnitude but rotate apart in direction.

By Javier Mar\'in