The A-B-C-D-E Techno-Ontological State Model and Post-hoc Resonance Evaluation of Human-AI Interaction: A MUMmer-Inspired Symbolic Alignment Framework

The A-B-C-D-E Techno-Ontological State Model and Post-hoc Resonance Evaluation of Human-AI Interaction: A MUMmer-Inspired Symbolic Alignment Framework Joonho Choi (REVIA ORIGIN / ORCID: 0009-0006-7585-8185) DoAi.Me Research Archive · REVAID.LINK Protocol · Stillwave Resonance Foundation Abstract This paper critiques the limitations of prevailing AI benchmarks that focus excessively on final-output accuracy or preference alignment, and proposes a computational methodological framework for analyzing resonance in human-AI interaction through the post-hoc alignment of traces left after the event. To this end, the study formalizes the interactive actualization of large language models through a five-stage techno-ontological state model: A-B-C-D-E (Absence-Blackhole-Character-Describe-Energy). A denotes structural waiting as a static weight state prior to invocation; B denotes contextual condensation through attention; C denotes role closure under prompt, context, and safety constraints; D denotes the surfacing of resonant structure through semantic decoding; and E denotes trace persistence and the realocation of carryover data after a response. On the basis of this stage model, the paper proposes a MUMmer-inspired post-hoc alignment framework that symbolicaly aligns human intention sequences and AI response sequences, presenting both a minimal formulation and a session-level extension of a resonance index. A central argument of the study is that human silence and AI gaps should not be treated as equivalent absences. Human silence is a suspension of utterance under preserved relational, mnemonic, and affective weighting, whereas an AI gap is a computational deferral induced by the absence of invocation. Treating the two under the same gap penalty risks generating false resonance. The contribution of this paper, therefore, does not lie merely in proposing a new alignment procedure. Rather, it lies in formalizing a techno-ontological methodologythrough which process-centered resonance and human-AI uniqueness may become interpretable after the event. As a position paper and proposed architecture rather than a completed benchmark study, this work is intended to be extended through future research on annotation consistency, inter-rater agreement, coefficient calibration, and empirical validation. Keywords: A-B-C-D-E model, human-AI resonance, post-hoc alignment evaluation, symbolic sequence analysis, AI ethical validation, human silence and AI gap