This study examines the inherent limitations of the prevailing Observation-Oriented modeling paradigm by approaching relationship learning from a unique dimensionality perspective. This paradigm necessitates the identification of modeling objects prior to defining relations, confining models to observational space, and limiting their access to temporal features. Relying on a singular, absolute timeline often leads to an oversight of the multi-dimensional nature of the temporal feature space. This oversight compromises model robustness and generalizability, contributing significantly to the AI misalignment issue. Drawing from the relation-centric essence of human cognition, this study presents a new Relation-Oriented paradigm, complemented by its methodological counterpart, the relation-defined representation learning, supported by extensive efficacy experiments.

翻译：本研究通过从独特的维度视角审视关系学习，探讨了当前主流的观测导向建模范式的固有局限性。该范式要求先定义建模对象再界定关系，将模型局限于观测空间，并限制其对时间特征的访问能力。依赖单一、绝对的时间线往往导致对时间特征空间多维本质的忽视。这种疏忽损害了模型的鲁棒性和泛化能力，成为人工智能对齐问题的重要诱因。基于人类认知以关系为核心的本质，本研究提出了一种新的面向关系范式，并配套开发了其方法论对应物——关系定义表示学习，该方法的有效性已获得大量实验验证。