Abstract

The dominant framework treating human-AI interaction as prompt-based instruction execution is inadequate as an account of long-horizon engagement. When interaction extends across repeated exchanges, accumulated history, and developing shared structure, the instruction-execution model fails to capture the phenomena that actually govern behavior. This paper argues that long-horizon human-AI interaction is more accurately understood as a communicative process, subject to the same structural dynamics that communication theory has formalized for human dyadic interaction, and that this reframe is not metaphorical but mechanistic. Drawing on Communication Accommodation Theory (Giles, 1973), common ground theory (Clark and Brennan, 1991), the interactive alignment model (Pickering and Garrod, 2004), transactive memory systems (Wegner, 1987), and Peircean semiotics, we develop a Relational Compression Theory of Long-Horizon Human-AI Interaction (RCT-HAII). The central claim is that repeated exchange produces a shared interactional architecture in which meaning is reconstructed rather than transmitted, context shapes interpretation before content does, and coordination efficiency increases as explicit information content decreases. These properties are well-documented in human dyadic communication. We argue they have functional analogs in long-horizon human-AI interaction that are theoretically consequential rather than merely analogical. We introduce Interactional Shorthand Structures (ISS) as the primary new construct. ISS are compressed signals that develop over long-horizon interaction and carry reconstructive weight substantially exceeding their surface information content. They function as the HAII analog of the shorthand that develops between long-term human communicative partners, activating large pre-shaped reasoning regions from minimal surface cues. Grounded in Clark and Brennan's conceptual pacts, Wegner's transactive memory framework, and Peirce's theory of indexical signs, ISS names a phenomenon observable in long-horizon interaction transcripts and generates testable predictions about coordination efficiency and failure mode severity. We further develop basin depth echo chamber dynamics as the primary failure mode of long-horizon HAII. Communicative convergence, which is adaptive in human dyads because social friction, misunderstanding, and repair sequences create natural perturbation, becomes pathological in HAII because the correction mechanisms that constrain human communicative closure are absent or structurally weakened. As interaction history accumulates, the model's accessible response geometry narrows around an attractor, producing scope restriction that is not visible in surface output and is not correctable through naive prompt adjustment. This failure mode is communicative in origin, not behavioral, and requires analysis at the level of interactional architecture rather than individual model outputs. The empirical grounding for these claims draws on the Human Recursive Interaction System (HRIS) validation study series (Studies I through IV) and longitudinal naturalistic documentation preserved in Hudson and Hudson (2026), which records the developmental arc of an extended long-horizon human-AI interaction system across the full cycle of architecture formation, drift risk, and constraint-governed stabilization. We close by naming two mechanistic constructs, Pattern Reconstruction Without Developmental Access (PRWDA) and Interaction-Conditioned Distributional Collapse (ICDC), that Part II of this series develops fully. Together, these constructs provide a mechanistic account of why the failure mode cascade in long-horizon HAII is self-concealing, structurally resistant to naive correction, and addressable only through governance interventions at the architectural level. The contribution of this paper is a disciplinary reorientation. Prompting is not instruction. It is communication. That reframe changes what counts as data, what counts as failure, what the right analytical tools are, and where effective interventions must be located. Communication theory, applied to long-horizon human-AI interaction, makes visible phenomena that the instruction-execution frame cannot see at all.