A Compositional Framework for Open-ended Intelligence
Researchers have introduced a new framework for open-ended intelligence, which is the ability to adapt to novel problems and environments beyond training data. This framework formalizes open-ended intelligence as a closure induced by a set of primitive elements and composition operators. The mathematics underpinning this framework requires both representational primitives (like states and actions) and algorithmic primitives, combined with composition motifs such as recursion and sequencing. The goal is to enable the generation of infinite adaptive responses across diverse settings and to foster architectures where compositional generalization is inherent. AI