What Is the"Self"of a Large Language Model?

Abstract

What could the"self"of a large language model be, if it is neither a particular word nor a single privileged layer? We propose a relational account: a functional self is an actor-centered organization of information that distinguishes the model's recorded actions from externally supplied material while content and reasoning operations change. Its representational candidate is a family of layer-specific source-binding components, rather than one unchanged vector throughout the network. Preliminary observations from a 27B BF16 model provide a geometric starting point. Across 150 matched message bodies, 450 role conditions, and a 75/75 discovery-held-out split, the combined Assistant-versus-external displacement transfers strongly across bodies and positions. Selected early components place User and System closer to one another than to Assistant, whereas this full-space distance relation does not persist throughout depth. These findings motivate a layered account: an early source-encoding candidate can be combined with role-specific information use and continuation preparation at later depths, without requiring monotonic overwriting of the common displacement. We connect this account to dynamic reasoning, historical continuity, and a computational model separating content operations, source binding, authority, and output. The reported evidence establishes a reproducible role-conditioned geometry; the proposed interpretation is that functional selfhood may reside in persistent relations among changing representations.