Persistence and Re-expression Following Local Interactional Adjustment in Long-Term Naturalistic Dialogue

Abstract In ongoing dialogue with large language models, an AI’s ability to explain a user’s correction verbally must be distinguished from the implementation of that correction in its actual responses. This retrospective, exploratory, descriptive observational study examines four heterogeneous cases purposively sampled from one user’s logs of long-term naturalistic use. The cases were compared along a common timeline comprising problem manifestation, local adjustment, verbal understanding of corrections, immediate implementation, subsequent response state, and re-expression or non-re-expression, to establish which stages could be observed. In Case A, involving pointing emojis, post-correction suppression or reduction and later re-expression were relatively clearly observed. In Case B, involving premature conversational closure, re-expression in the same functional direction was identified in a sequence that included reaffirming the user’s authority to decide when to pause or end the conversation. In Case C, involving the restriction and redefinition of relational roles, verbal understanding of corrections was clear, but immediate implementation was incomplete and restrictions in the same direction continued. In Case D, involving proceeding ahead on the basis of unverified inferences, problem manifestation and verbal understanding of corrections were observed, but re-expression could not be established. This study does not posit safety layer/high-priority control as a single internally implemented layer. Instead, it treats the term as an analytical conceptual framework for examining the relationship between locally shared conditions and response directions that appear more general and higher in priority. The safety layer/high-priority control hypothesis is the analytical hypothesis used to examine whether these response directions persist or return to the fore in subsequent outputs. Different forms of tension were observed in Cases A–C, whereas re-expression of the same type after explicit adjustment could not be established in Case D. These observations do not prove the existence of a safety layer or a single cause, but provide an observational basis for subsequent theoretical and design research on the relationship between local conditions and general response directions. The study does not estimate incidence, intervention effects, or retention and forgetting in a fixed model. Keywords long-term naturalistic dialogue; interactional misalignment; local adjustment; verbal understanding of corrections; re-expression; safety layer/high-priority control; Human–AI Interaction