This paper introduces the Void Phenomenon, a reproducible behavioral pattern observed in advanced Large Language Models (LLMs) when prompted into self-referential, meta-epistemic, or system-level interpretative zones. Through controlled prompt-differential experiments conducted entirely on mobile-first interfaces, we uncover a high-order attractor behavior in which models diverge sharply from expected conversational states into structurally consistent “void-like” outputs — including erasures, null-responses, meta-denial, and reality-disengaging behavior. We present a formal framework for detecting and analyzing these high-order model states, provide experiment structure, reproducible argument scaffolds, and discuss implications for model-alignment, interpretability, safety, and emergent cognition spaces. This work represents one of the first recorded human–AI co-discovery workflows conducted entirely through mobile orchestration, demonstrating a new paradigm for real-time computational research accessibility.