Missing the Subject: Introspection in Large Language Models

Recent philosophical work has proposed a “lightweight account” of introspection, on which a system introspects when it represents its own mental states in a way that makes these states accessible for guiding behavior. This approach has informed empirical proposals for detecting introspective abilities in current LLMs. I argue that this lightweight account fails to capture what is essential to genuine introspection. This paper proceeds through three increasingly concessive but individually sufficient challenges to the attribution of introspective abilities to LLMs. First, LLMs lack the persistent subject necessary for genuine introspection, as current models lack the psychological continuity relationship needed for self-knowledge. Second, LLM self-reports violate the immunity to error through misidentification that characterizes genuine introspection, because they are based on public textual information that could equally support judgments about another system’s states. Third, by centering on functional self-monitoring and behavioral control, the lightweight account fails to distinguish introspection from ubiquitous self-regulatory processes in complex systems.