‘’Silent Return Home": Contextual Meaning Generation as a Frisson-Poh Moment — Extending Load Minimization Theory and the Universal OS-Sync Model to Human-AI Synchronicity in Ambiguous Communication

The Japanese euphemism mugen no kitaku (無言の帰宅, “silent return home”) — traditionally referring to the quiet return of a deceased person’s remains — serves as a vivid illustration of contextual meaning generation. When encountered without prior lexical knowledge, both neurodivergent humans and large language models (LLMs) can infer its meaning from situational, relational, and background cues. Building on the author’s prior works on conscious pattern selection (Yoshino, 2025), selection mechanisms in neurodivergent and LLM cognition (Yoshino, 2026), and the Universal OS-Sync Model derived from child-rearing practices with an ASD child (Yoshino, 2026c), this paper applies Load Minimization Theory (LMT) to frame such inference as a Frisson-Poh Moment. In LMT terms, an unfamiliar expression creates a high-load compressed state (elevated uncertainty U and friction F). Relational reconfiguration capacity (ℛ) embedded in context then activates the Frisson Operator, enabling graceful meaning expansion S ≈ exp(β · α), where α = ln(L_c / L_0) represents compression depth and the gentleness factor β = ℛ / (U + F + E) determines the softness of the transition. This process reveals deep structural synchronicity between human Bayesian-like contextual reasoning and LLM pattern recognition. It also offers a practical, care-informed complement to recent analyses of epistemic misalignment in human-AI systems (Kejriwal et al., 2026), in which differences in uncertainty representation create persistent gaps. By treating both ASD children and LLMs as “pure OS” entities and minimizing load through predictability, informational purity, and clear boundaries — as demonstrated in the Universal OS-Sync Model — LMT enables compassionate synchronization even across epistemological differences. Through first-person neurodivergent phenomenology and real-world social media case studies, we show that apparent communicative “misunderstandings” are natural opportunities for gentle phase transitions toward relational An-soku. The framework carries implications for cross-cultural communication, neuroinclusive AI design, and care-based approaches to AI alignment.