Abstract

The study of large language model behavior has focused almost exclusively on the model: its architecture, its training data, its parameters, its benchmark performance. This paper argues that focus is misplaced. The behavior that matters most in practice does not live in the model. It lives in the interaction. This is not a theoretical claim. It is an empirical one. Observations across extended human-LLM interaction document that structured linguistic input, introduced consistently across turns, produces stable, recoverable reasoning trajectories that cannot be predicted or explained by examining the model in isolation. The interaction is not a delivery mechanism for instructions. It is the primary site where behavior is shaped, stabilized, and constrained. The Hudson Recursive Interaction System (HRIS) is an empirically grounded framework developed to describe this phenomenon. It documents how constraint regimes introduced through structured interaction stabilize reasoning trajectories in high-dimensional semantic space, how those trajectories exhibit resistance to perturbation, and how previously established interaction patterns enable rapid re-entry into coherent reasoning without reliance on persistent memory. These are interaction-level phenomena. They are invisible to model-level analysis. Once the interaction is treated as the primary unit of analysis, findings from multiple independent disciplines fall into place. Communication theory, psycholinguistics, rhetoric, semiotics, formal semantics, and information theory have each described pieces of this dynamic from their own angles. None has applied it systematically to human-LLM interaction. This paper does. The central claim is simple: if you want to understand how large language models actually behave in use, stop looking at the model. Start looking at the interaction.