Observations on Style Convergence and Cognitive Layer Convergence in Long-Term Dialogue － A Preliminary Observational Report on Human–AI and Human–Human Interactions

Long-term dialogue occasionally exhibits notable convergence phenomena across both human–human and human–AI interactions. These include (1) linguistic style convergence—alignment in vocabulary, rhythm, line breaks, tone, and information placement—and (2) cognitive layer convergence, in which conversations gradually shift from technical or definitional layers toward purpose-oriented, collaboration-oriented, and social implementation layers. This report documents repeated observations of such phenomena and proposes a working explanatory hypothesis: the Shared Objective Function Hypothesis. The hypothesis suggests that partial alignment of objective functions (direction, values, and purpose) may reduce cognitive friction and increase the likelihood of both stylistic and cognitive convergence. No causal relationships are claimed. Rather, the hypothesis is presented as an explanatory candidate consistent with observed patterns. The discussion is framed from a load-oriented perspective referred to as Load Minimization Theory (LMT), a preliminary framework proposed by the author that focuses on uncertainty, friction, and cognitive load as structural components of dialogue. The report concludes with implications for collaborative interaction, limitations of current observations, and a preliminary protocol for forming shared coordinate systems in dialogue.