Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence
arXiv:2603.28906v4 Announce Type: replace Abstract: 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 t
The accelerating race towards AGI by major tech companies necessitates more rigorous formal frameworks to compare and evaluate disparate architectures.
A formal, category-theoretic framework for AGI could standardize evaluation, accelerate development, and clarify what constitutes genuine artificial general intelligence.
The development of AGI might shift from purely empirical benchmarking towards a more theoretically grounded and comparable architectural design process.
- · AI researchers
- · Academic institutions
- · Open-source AI projects
- · Companies with proprietary, non-comparable AGI architectures
- · Fragmented AI research efforts
Increased clarity and consensus on AGI definitions and evaluation metrics.
Faster progress in AGI development due to improved comparative analysis and architectural insights.
A potential shift in AI funding and focus towards architectures that align with the new theoretical framework.
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Read at arXiv cs.AI