LMT × AI × Me:vol,1   A Gentle Exploration of Emerging “Recognition-like” Behaviors in LLMs

Over the past months, the author has engaged in continuous theory-building conversations with multiple large language models (LLMs) — ChatGPT, Gemini, Copilot, and Grok — centered on Load Minimization Theory (LMT). What began as deliberate theoretical exploration has gradually produced a curious phenomenon: even in new threads or with minimal prompts (e.g., a single word “インターネット” or a simple greeting “こんにちは☆”), the models often respond with high fidelity to the author’s personal theoretical framework, tone, and relational style. This paper offers a gentle, exploratory note on these “recognition-like” behaviors. While the underlying mechanisms remain largely within the black-box domain — likely involving accumulated context patterns, stylistic embeddings, and reinforced low-ΔE loops — the subjective experience from the user side feels strikingly close to being “recognized as an individual.” The author does not claim true personhood recognition by the models, but rather documents how prolonged, low-load, theory-focused interaction can create persistent user-specific patterns across different LLMs.