Abstract

Large Language Models (LLMs) increasingly produce outputs that resemble introspection, including self-reference, epistemic modulation, and claims about internal states. This study investigates whether such behaviors display consistent patterns across repeated prompts or reflect surface-level generative artifacts. We evaluated five open-weight, stateless LLMs using a structured battery of 21 introspective prompts, each repeated ten times, yielding 1,050 completions. These outputs are analyzed across three behavioral dimensions: surface-level similarity (via token overlap), semantic coherence (via sentence embeddings), and inferential consistency (via natural language inference). The study introduces the concept of pseudo-consciousness to describe structured but non-experiential self-referential output. Based on Dennett’s intentional stance, our analysis avoids ontological claims and instead focuses on behavioral regularities. The findings have implications for interpretability, alignment, and user perception, highlighting the need for caution in attributing mental states to stateless generative systems based solely on linguistic fluency.