Deep Understanding: From Signal Weight Recognition to Ontological Derivation of AI Architecture

This paper argues that current large language models operate within what we term a deep learning framework — a system that treats frequency of pattern occurrence as a proxy for signal weight. While achieving impressive performance on well-represented tasks, it is structurally incapable of recognizing low-frequency, high-weight signals: the rare insight, the user's actual intent buried beneath their words, the crucial detail that contradicts the expected pattern. We call the capacity to recognize such signals deep understanding, and we propose that it requires a fundamentally different architecture — one grounded in an ontological framework that places the human anchor point at the center of the system. Drawing on concrete failures observed in practice, classical Chinese philosophical examples, and the Meta-Origin Theory (元本论) framework, we derive the basic principles and structural requirements of a deep understanding model.