Relation-Oriented: Toward Causal Knowledge-Aligned AGI

Observation-Oriented paradigm currently dominates relationship learning models, including AI-based ones, which inherently do not account for relationships with temporally nonlinear effects. Instead, this paradigm simplifies the "temporal dimension" to be a linear observational timeline, necessitating the prior identification of effects with specific timestamps. Such constraints lead to identifiability difficulties for dynamical effects, thereby overlooking the potentially crucial temporal nonlinearity of the modeled relationship. Moreover, the multi-dimensional nature of Temporal Feature Space is largely disregarded, introducing inherent biases that seriously compromise the robustness and generalizability of relationship models. This limitation is particularly pronounced in large AI-based causal applications. Examining these issues through the lens of a dimensionality framework, a fundamental misalignment is identified between our relation-indexing comprehension of knowledge and the current modeling paradigm. To address this, a new Relation-Oriented} paradigm is raised, aimed at facilitating the development of causal knowledge-aligned Artificial General Intelligence (AGI). As its methodological counterpart, the proposed Relation-Indexed Representation Learning (RIRL) is validated through efficacy experiments.