Non-Convergence and Interpretive Saturation: An Observation Record on Cross-Domain Critique and Public Interpretation in NarrQuest

This observation record examines two related empirical observations emerging from the NarrQuest project. The first observation concerns a set of cross-domain critiques of NarrQuest presented within the same issue. Rather than converging toward a unified evaluation, these critiques diverge along fundamentally incompatible epistemic regimes, including control-theoretic observability, financial convergence, and embodied cognition. This divergence is not interpreted as ordinary disagreement within a shared framework, but as a condition of non-convergence, where no common evaluative standard exists to adjudicate validity across domains. The second observation concerns public interpretations of AI behavioral instability collected through the NarrQuest Interpretation Layer. Across sixty-eight comments, participants employed diverse vocabularies drawn from sociology, philosophy, AI safety, prompt engineering, probabilistic reasoning, and everyday experience. Despite this interpretive diversity, no new structural interpretation category emerged after the initial phase of observation. Public explanations repeatedly converged into a limited set of recurring interpretive structures. Taken together, these observations reveal an asymmetry between expert critique and public interpretation. While cross-domain expert critiques exhibited persistent non-convergence, public interpretations displayed increasing saturation and structural convergence. This record does not seek to resolve either condition. Instead, it documents the coexistence of epistemic non-convergence and interpretive saturation as observable features of contemporary AI discourse. In this context, NarrQuest functions neither as a unifying framework nor as a theory of explanation. Rather, it serves as an observational site through which the limits of cross-domain adjudication and the stabilization of public interpretation structures become visible.