{"slug": "category-theoretic-comparative-framework-for-artificial-general-intelligence", "title": "Category-Theoretic Comparative Framework for Artificial General Intelligence", "summary": "Researchers have proposed a category-theoretic framework for comparing artificial general intelligence architectures, aiming to provide a formal foundation for AGI systems. The framework, detailed in a working paper, allows for the analysis of architectures such as reinforcement learning and causal RL, highlighting commonalities and differences. This work seeks to address the lack of a formal definition for AGI and support future research.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 30 Mar 2026 (\n\n[v1](https://arxiv.org/abs/2603.28906v1)), last revised 4 May 2026 (this version, v3)]# Title:Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence\n\n[View PDF](/pdf/2603.28906)\n\n[HTML (experimental)](https://arxiv.org/html/2603.28906v3)\n\nAbstract: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.\n\n## Submission history\n\nFrom: Pablo De Los Riscos [[view email](/show-email/a75dc1a6/2603.28906)]\n\n**Mon, 30 Mar 2026 18:34:27 UTC (315 KB)**\n\n[[v1]](/abs/2603.28906v1)**Wed, 8 Apr 2026 17:12:32 UTC (322 KB)**\n\n[[v2]](/abs/2603.28906v2)**[v3]** Mon, 4 May 2026 10:02:13 UTC (359 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/category-theoretic-comparative-framework-for-artificial-general-intelligence", "canonical_source": "https://arxiv.org/abs/2603.28906", "published_at": "2026-06-29 06:13:09+00:00", "updated_at": "2026-06-29 06:28:36.186027+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-safety", "ai-ethics"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/category-theoretic-comparative-framework-for-artificial-general-intelligence", "markdown": "https://wpnews.pro/news/category-theoretic-comparative-framework-for-artificial-general-intelligence.md", "text": "https://wpnews.pro/news/category-theoretic-comparative-framework-for-artificial-general-intelligence.txt", "jsonld": "https://wpnews.pro/news/category-theoretic-comparative-framework-for-artificial-general-intelligence.jsonld"}}