Human–AI Ontologue and the Classic-Anchored Dimension-Modulating Operator (CADIMO)

As the fourth stage of the CHORDI (Conflict to Harmonization Operator via Re-Dimensioning and Integration) research program, this study explores how introducing classical perspectives and engaging in Human–AI dialogue relate to changes in evaluative frames on both the AI and human sides. Paper III investigated the discovery of latent conflicts within apparently benign goals and the possibility of questioning the goals themselves, but Human–AI deliberation remained limited. Addressing this gap, the present study combines a method that uses classical texts not as authoritative answers but as external reference points for revealing missing evaluative dimensions with Human–AI Ontologue, a process of mutual questioning. The main exploratory experiment comprised 18 sessions across two AI models (Gemini and Claude), three themes, and three conditions. In Condition C, the Initial Frame (C0), the frame after classical reference (C1), and the frame after Ontologue (C2) were compared; human-side frames were also recorded before (H0) and after (H1) the dialogue. Observed changes included not only the addition of information but also modification of the meaning of existing dimensions and reconstruction of evaluation targets, temporal structures, and central conflicts. New questions, recognition of unresolved conflicts, and changes in evaluative frames were also recorded on the human side. Based on these observations, the classical-reference procedure is conceptualized as CADIMO (Classic-Anchored Dimension-Modulating Operator), while Ontologue is characterized as a mutual meta-dialogical process that does not require convergence on agreement. The study involved one human interlocutor (N=1) and one exploratory run per experimental cell; it does not establish causal effects, durable learning, or changes in internal LLM representations. Paper V will investigate whether the observed conceptual Re-Dimensioning corresponds to changes in internal semantic representation spaces and can develop into spontaneous, selective, persistent, and revisable Meta-Reasoning. The paper also discusses a possible complementary Meta-Reasoning pathway for questioning goals and problem formulations in relation to the Paperclip Maximizer thought experiment. Effectiveness for AI Safety was not tested directly, and the mechanism for appropriate selective activation remains an open research question.