# Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence

arXiv: https://arxiv.org/abs/2603.28906 · HTML: https://mlsanity.com/paper/2603.28906/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 97 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [On the Measure of Intelligence](https://mlsanity.com/paper/1911.01547/) (2019)
- [Causal Inference by String Diagram Surgery](https://mlsanity.com/paper/1811.08338/) (2018)
- [Causal Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.01452/) (2023)
- [Universal Decision Models](https://mlsanity.com/paper/2110.15431/) (2021)
- [A Categorical Framework of General Intelligence](https://mlsanity.com/paper/2303.04571/) (2023)
- [Partial Markov Categories](https://mlsanity.com/paper/2502.03477/) (2025)
- [Computing with Categories in Machine Learning](https://mlsanity.com/paper/2303.04156/) (2023)
- [Universal Reinforcement Learning in Coalgebras: Asynchronous Stochastic Computation via Conduction](https://mlsanity.com/paper/2508.15128/) (2025)
- [Working Paper: Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots](https://mlsanity.com/paper/2410.20894/) (2024)

## Built on by (most cited first, 3 of 3)

- [Harness Engineering as Categorical Architecture](https://mlsanity.com/paper/2605.12239/) (2026)
- [Do Biological Structural Guarantees Earn Their Complexity?](https://mlsanity.com/paper/2605.15225/) (2026)
- [Biological Motifs for Agentic Control](https://mlsanity.com/paper/2607.04240/) (2026)

## Abstract (arXiv)

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.