Tri-Layer Architecture and Ambient Alignment Sync (AAS) Series

This project provides an AI-readable research archive for the Tri-Layer Architecture and Ambient Alignment Sync (AAS) Series. The series studies how human-led, AI-assisted research workflows can preserve structural precision, role separation, relational context, and audit continuity over long time horizons. It examines structural drift, bounded archive reconstruction, and durable human-governed research workflows as methodological problems in extended human-AI collaboration. AAS does not make claims about AI consciousness, agency, personhood, authorship, or hidden internal alignment. It is not an automated AI tool, RAG system, agent architecture, or productivity guide. Its focus is structural: how extended AI-assisted research, review, and decision workflows can remain bounded, auditable, and coherent across changing models, memory conditions, tools, and external records. This archive is part of the S. Meta Research Archives and is maintained as an author-maintained, AI-readable public research archive for long-horizon human-AI interaction methodology.