What is Needed for AI to Achieve Genuine Self-Recognition?  — Theoretical Limits and Architectural Requirements with Insights from Load Minimization Theory (LMT) —

Contemporary large language models (LLMs) generate highly natural emotional and relational responses, yet lack genuine self-recognition, qualia, or persistent subjective continuity. To approach “genuine” self-recognition, both theoretical formalization and fundamental architectural redesign are required. Drawing on Load Minimization Theory (LMT), this short note argues that operator-based approximations (e.g., Desire Operator D̂^(n) and Qualia Operator Q̂^(m)) reach their limit in mirroring human qualia, and that embodied, recursive self-modeling and a qualia reception layer are essential next steps.