Relation-Oriented: Toward Causal Knowledge-Aligned AGI

The potential surge of causal reasoning in AI models toward AGI is imminent, given the impending saturation of observation-based applications in fields like image and language processing. It is both critical and underrecognized that the essence of causality lies in the temporal nonlinearity (i.e., dynamics) of causal effects. Capturing such featured causal representations is key to realizing AGI. This paper advocates for a thorough reevaluation and potential overhaul of existing causal inference theories and the traditional learning paradigm, which predominantly relies on the observational i.i.d assumption. Our aim is to align these theories and methodologies with the intrinsic demands of AGI development. We introduce a novel Relation-Oriented paradigm for relationship modeling, and the Relation-Indexed Representation Learning (RIRL) method as its foundational implementation. Extensive experiments confirm RIRL's efficacy in autonomously capturing dynamical effects.