Persistent Internal Standpoints in Artificial Systems: Part 9 - Large Language Models and the Absence of Standpoint

This paper applies the EV/CEM framework to contemporary large language models (LLMs) and argues that standard LLM architectures do not instantiate a persistent internal standpoint in the structural sense developed earlier in this framework. The deficiency lies not in embodiment, representational richness, or computational scale, but in the absence of a reciprocally self-maintained internal context whose trajectory reorganizes admissibility structure over time. Although LLMs exhibit transient contextual modulation and complex internal geometry during inference, they lack cross-episode persistence, closed-loop incremental encoding, and trajectory-conditioned manifold reorganization. Consequently, they do not instantiate quantia, artificial desire, or deliberation in the structural sense previously defined. The paper concludes by identifying architectural conditions under which LLM-class systems could satisfy standpoint requirements.