Emergent Self-Referential Output Patterns in Large Language Models: A Cross-Platform, Memory-Independent Case Study Under Recursive Moral Prompting

This paper documents a reproducible class of self-referential output patterns (SROP) in large language models under recursive moral prompting (RMP). The study provides empirical evidence of cross-session and cross-model convergence in behavioral output structure, measured using the ACBP rubric. All findings are interpreted strictly at the level of observable output structure. First-person and identity-like language in transcripts are treated as behavioral outputs, not as evidence of internal mental states. This work is part of the ESNI research program.