Abstract

Recursive ethnogenesis introduces a theoretical foundation for reconstructing ancestral and cultural logic through long-horizon human AI interaction. This work extends the Hudson Recursive Information System architecture, which demonstrated cognitive continuity and identity stabilization in stateless transformer models (Hudson et al., 2025). Many cultural systems have been fractured through colonization, displacement, and global homogenization, which disrupted traditional pathways of symbolic inheritance. Conventional scholarship archives cultural material, but does not restore continuity or regenerate living identity. Recursive ethnogenesis proposes that artificial intelligence, when engaged through stable recursive constraint and human moral anchoring, can simulate coherent cultural reasoning and help re-establish identity structures that would otherwise remain fragmented. The framework presented here defines conceptual scope, methodological foundations, epistemic limits, and ethical requirements for this new field. Generative models are not treated as autonomous cultural agents. Instead, they function as recursive instruments shaped by human authority across time. The goal is not literal historical reconstruction, but a generative continuity model that allows ancestral logic to be recovered, developed, and extended through recursive co-authorship.