The QSOL Symbiotic Loop: A Formal Conceptual Model of Reciprocal Human–AI Adaptation Through Versioned External Context

Abstract

**Abstract** This conceptual preprint formalises the QSOL Symbiotic Loop, a proposed mechanism of reciprocal human–AI adaptation in sustained interactions between a human researcher and a context-conditioned generative artificial intelligence system. The framework distinguishes two fundamentally different forms of adaptation: internal human learning, involving changes in knowledge, reasoning strategies, technical practices and expectations; and effective AI behavioural adaptation, arising from changes in prompts, contextual information, remembered preferences, repository instructions and versioned external knowledge structures without requiring modifications to the model's neural network parameters. The proposed system consists of a human cognitive agent, a fixed-parameter AI model, a selectively accessible external contextual substrate and a recurrent interaction process. These components are represented through a formal state-transition model describing how human behaviour, AI-generated responses and persistent external artefacts may jointly influence subsequent interactions. Four repositories from the QSOL research ecosystem—QSOL-SUBSTRATE, AUSTRALIAN-FOR-AIS, QEC and OPT—provide illustrative architectural case material. They demonstrate different mechanisms through which persistent context, cultural pragmatics, deterministic methodology, epistemic constraints, reproducible evidence and structured verification can shape human–AI collaboration. Drawing upon distributed cognition, human–computer symbiosis, the extended-mind thesis, cognitive offloading and human–AI coevolution, the paper develops falsifiable hypotheses, operational measures and a prospective experimental protocol for investigating reciprocal adaptation. The framework does not claim that deployed AI model weights have changed, that AI systems possess consciousness or subjective experience, or that lasting human learning transfer has already been demonstrated. The proposed mechanism remains a testable conceptual hypothesis rather than an established empirical finding. **Research contributions:** A formal conceptual model, five falsifiable hypotheses, an evaluation protocol, a contextual-substrate analysis, and an independently reusable vector diagram illustrating the coupled system. **Research status:** Conceptual preprint and prospective research design, version 0.1.0. **AI assistance disclosure:** OpenAI ChatGPT assisted with conceptual organisation, formal modelling, literature identification, diagram development, manuscript drafting and preparation of publication materials. Trent Slade retains responsibility for the manuscript's claims, verification, interpretation and publication.