The Principles of Hallucination Suppression and Their Relationship to the Structural Characteristics of Human–AI Communication

This work presents a structural hypothesis explaining why hallucinations fail to occur in certain long-term human–AI interaction cases, despite using general-purpose large language models (LLMs) under ordinary deployment conditions. Rather than treating hallucinations as unavoidable artifacts of probabilistic generation, this study reframes them as phenomena arising from misalignment in problem formulation, assumption sharing, and cognitive-layer transitions during dialogue. The analysis is grounded in empirical observations from sustained human–AI interaction where hallucinations were not observed, and proposes an explanatory model based on the decomposition of communication structure and cognitive layers. Importantly, this work does not: propose new training methods, modify internal model architectures, introduce new ethical rules or value systems, nor claim to eliminate hallucinations entirely. Instead, it aims to clarify: at which stage hallucinations emerge in the communication process, why they occur more frequently in general usage environments, and under what conditions they can be structurally suppressed. To address these questions, the paper introduces the concepts of Relational Design and Relational Coherence as design and operational conditions that make mismatches in assumptions, hypotheses, and cognitive states observable and correctable within dialogue. These concepts do not directly “remove” hallucinations. Rather, they provide a communicative structure that maintains undefined or partially shared assumptions as valid cognitive states, preventing premature closure during explanation. This study positions hallucinations not as anomalous failures to be eliminated, but as explainable outcomes of interaction structure—offering a cognitive and structural basis for re-evaluating hallucination suppression beyond purely engineering-based approaches. Project Scope and Interpretation Notice This project presents a theoretical and explanatory framework for understanding hallucination phenomena in human–AI communication. The core contribution of this work is not the proposal of new learning methods, model architectures, prompt techniques, or optimization strategies. Instead, it reframes hallucination as a phenomenon emerging from misaligned assumptions, unresolved premises, and cognitive-state inconsistencies within interactional structures, rather than as a simple generation error or probabilistic failure. Accordingly, this research does not aim to: eliminate hallucinations completely, improve output accuracy through optimization, prescribe deployable methods, best practices, or implementation recipes, or modify internal model mechanisms. The concepts of relational design and relational coherence are introduced solely to explain why hallucinations fail to appear in certain interactional conditions and emerge in others, based on observed long-term dialogue cases in general LLM environments. This framework is descriptive, not prescriptive. Any interpretation that treats this work as a technique, operational guideline, or system instruction falls outside its intended scope. Responsibility for interpretation, validation, implementation, and any resulting outcomes lies entirely with the implementing party. Any practical application requires independent technical verification and ethical review by the user or organization adopting it.