Epistemic Coprocessors and the Emergence of Stable Human-model Dyads: Toward an Architecture of Asymmetric Human-AI Interaction

Abstract

Recent generations of Large Language Models have enabled a new form of human–machine interaction: relatively stable epistemic dyads between human actors and probabilistic language systems. 

This paper introduces the concept of the asymmetric epistemic dyad as a preliminary analytical framework for describing recursive human–model interaction architectures within probabilistic epistemic environments. 

The paper argues that the decisive transition is not anthropomorphic “machine intelligence,” but the emergence of interaction architectures in which models function as epistemic coprocessors. Within such dyads, meaning integration, normative evaluation, intentional direction, and epistemic responsibility remain located on the human side, while models contribute probabilistic structural processing, recursive reconstruction, semantic variation, and stabilization of extended epistemic operations. 

The framework distinguishes these interaction architectures from prompting, conventional software augmentation, and earlier models of distributed cognition. 

The paper does not claim that language models possess consciousness, intentionality, autonomous understanding, or independent epistemic agency. Nor does it claim finalized empirical validation for the proposed framework. The analysis is exploratory, architectural, and conceptual. 

Its primary contribution lies in defining a conceptual observation and analysis space for investigating long-form recursive human–AI interaction under conditions of sustained epistemic continuity. 

The document should therefore be understood as an exploratory working framework situated within the broader discourse on human–AI interaction, epistemic infrastructure, and probabilistic cognitive environments.

Keywords: Human–AI Interaction, Epistemic Coprocessors, Asymmetric Epistemic Dyads, Recursive Interaction, Epistemic Infrastructure, Probabilistic Systems, Long-form Interaction, Distributed Cognition, Recursive Stabilization, Human–model Collaboration