AGI has become the Holly Grail of AI with the promise of level intelligence and the major Tech companies around the world are investing unprecedented amounts of resources in its pursuit. Yet, there does not exist a single formal definition and only some empirical AGI benchmarking frameworks currently exist. The main purpose of this paper is to develop a general, algebraic and category theoretic framework for describing, comparing and analysing different possible AGI architectures. Thus, this Category theoretic formalization would also allow to compare different possible candidate AGI architectures, such as, RL, Universal AI, Active Inference, CRL, Schema based Learning, etc. It will allow to unambiguously expose their commonalities and differences, and what is even more important, expose areas for future research. From the applied Category theoretic point of view, we take as inspiration Machines in a Category to provide a modern view of AGI Architectures in a Category. More specifically, this first position paper provides, on one hand, a first exercise on RL, Causal RL and SBL Architectures in a Category, and on the other hand, it is a first step on a broader research program that seeks to provide a unified formal foundation for AGI systems, integrating architectural structure, informational organization, agent realization, agent and environment interaction, behavioural development over time, and the empirical evaluation of properties. This framework is also intended to support the definition of architectural properties, both syntactic and informational, as well as semantic properties of agents and their assessment in environments with explicitly characterized features. We claim that Category Theory and AGI will have a very symbiotic relation.

翻译：通用人工智能（AGI）已成为人工智能领域的圣杯，其承诺实现与人类水平相当的智能，全球主要科技公司正投入前所未有的资源竞相追逐。然而，目前尚不存在统一的正式定义，仅有若干经验性AGI基准评估框架。本文旨在发展一个通用的、代数和范畴论框架，用以描述、比较和分析不同可能的AGI架构。该范畴论形式化方法将支持对RL、通用AI、主动推理、CRL、基于模式的认知学习等候选AGI架构进行比较，明确揭示其共性与差异，更重要的是，指出未来研究的空白领域。从应用范畴论视角出发，我们借鉴"范畴中的机器"概念，为AGI架构提供现代观点。具体而言，这篇立场论文一方面首次对范畴中的RL、因果RL和SBL架构进行形式化建模，另一方面标志着更广泛研究计划的起点——该计划旨在为AGI系统构建统一的正式基础，整合架构结构、信息组织、智能体具现化、智能体-环境交互、行为随时间演化及经验性属性评估。本框架还支持定义架构的句法属性、信息属性以及智能体的语义属性，并支持在具有明确特征化属性的环境中进行评估。我们主张范畴论与通用人工智能将形成高度共生的关系。