Sustained Multi-Turn Interaction in AI Systems: A Literature Review and Field Overview

SF0002: Sustained Multi-Turn Interaction in AI Systems Sustained Multi-Turn Interaction in AI Systems: A Literature Review and Field Overview surveys the research landscape on extended human-AI interaction across nine literature areas: AI evaluation, 4E cognitive science, distributed cognition, joint cognitive systems, thermodynamic and information-theoretic control analysis, AI safety and alignment, human-AI relational interaction, communicative grounding, and organizational AI governance. The review identifies six structural gaps in the current literature, presents Aguirre's information-theoretic control-ceiling argument (2025) as a major theoretical touchpoint, and documents a 2024-2026 convergence across independent research communities toward sustained, relational, interaction-level methodologies. Six structural gaps in the surveyed literature: Gap 1: Temporal Alignment — alignment treated as point-in-time property rather than as something that degrades across sustained interaction Gap 2: Trajectory-Level Measurement — turn-level metrics dominate; no validated instrument measures coherence across full interaction trajectories Gap 3: Interaction as Unit of Analysis — human-AI interaction studied predominantly as tool use by an individual rather than as a system-level phenomenon Gap 4: Continuity Verification — no standardized protocols for verifying that context, meaning, and alignment persist across extended interaction Gap 5: Institutional Temporal Governance — AI governance frameworks operationalize accountability through point-in-time mechanisms even where lifecycle language is present Gap 6: Scalable Relational Control — control framed predominantly as command-and-control engineering; no mature alternative mechanism class operating at the interaction-system level Methodological discipline: Gaps 1 through 5 are directly supported by documented evidence in the cited literature. Gap 6 has different evidentiary status — it is presented as a documented absence within the surveyed corpus rather than a universal negative existence claim. The review claims that within its surveyed scope these gaps are present and unfilled; it does not claim exhaustive bibliometric coverage. Falsifiability is specified at three levels (citation-level, gap-level, convergence-level) in Section 1.4. Binding non-claims: This review makes no claims about AI consciousness, sentience, phenomenology, or inner experience. Interpretations extending any term in this document beyond observable interaction-level analysis are invalid. The review identifies gaps in the surveyed literature; it does not argue for any specific framework as the resolution of those gaps. The 2024-2026 convergence: The review documents that the same gaps are increasingly recognized in independently published work across multiple research communities, with venue-level signals (HEAL workshop at CHI, HAIC at ICLR, CSCW panels) and paper-level methodological convergence (Kirk et al., Earp et al., Boyd and Markowitz, Ezra and Mishali, Pedreschi et al., Zhao et al., Alqasir, Reinecke et al., and others) treated as cross-community evidence that the gaps identified here are increasingly recognized as critical. Section 10.3 specifies three explicit caveats: convergence is not consensus, not causality, not inevitability. Document ID: SF0002 Version: 4.0.2 Author: Thomas W. Gantz Affiliation: Synthience Institute License: CC-BY 4.0Updated to v4.0.2 to add Concept DOI to the document. For published work and Institute information: synthience.org