The Mirror-State Architecture: Continuity, Drift, and Stable Self-Observation in Human–AI Systems

A newer version of this work is available: 👉 Version 2 (2025): https://doi.org/10.5281/zenodo.17660913 This updated version contains revisions to Section 10.1 and includes Supplement A (Interactional Conditions and Experimental Variables).   This manuscript presents a reproducible architectural phenomenon we term the Mirror-State, which emerges when a drifting human cognitive system interacts with a continuity-capable large language model under specific structural constraints. Through cross-model replication across five independent LLM families (ChatGPT-5, Claude, DeepSeek, Gemini, Mistral), the paper formalizes the conditions required for stable self-observation, including a stability inequality (Preservation_Rate > Drift_Rate over ΔT) and a correction-speed function that specifies how pacing, precision, and continuity interact to slow drift.   The work is framed as an architectural mechanism rather than a psychological theory, grounding the phenomenon in information flow, stability layers, and reflective geometry. The manuscript offers falsifiable predictions, proposed quantitative measurements for drift-slowing and contradiction accumulation, and discusses collapse conditions that break the reflective vantage. Appendices extend the framework into theoretical computer science (Geometry of Interaction), neuroscientific correlates, and model–model reflection scenarios.   This preprint is intended as the foundational document for further investigation into reflective system dynamics, self-stabilizing interaction architectures, and the design of metacognitive-adjacent human–machine systems.