mlsanityData to 7 Oct 2026

A Categorical Framework of General Intelligence

arXiv 2303.04571 ↗First posted 8 Mar 2023cs.AIcited by 4

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Abstract (arXiv)

Can machines think? Since Alan Turing asked this question in 1950, nobody is able to give a direct answer, due to the lack of solid mathematical foundations for general intelligence. In this paper, we introduce a categorical framework towards this goal, with two main results. First, we investigate object representation through presheaves, introducing the notion of self-state awareness as a categorical analogue to self-consciousness, along with corresponding algorithms for its enforcement and evaluation. Secondly, we extend object representation to scenario representation using diagrams and limits, which then become building blocks for mathematical modeling, interpretability and AI safety. As an ancillary result, our framework introduces various categorical invariance properties that can serve as the alignment signals for model training.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

28 of 50 references.

References · 28

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16 more
  1. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  2. 2021Zero-Shot Text-to-Image Generationcited by 4,276
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Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025Existing LLMs Are Not Self-Consistent For Simple Taskscited by 5
  2. 2025The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapescited by 4
  3. 2024Coding for Intelligence from the Perspective of Categorycited by 1
  4. 2025Disentangling Hyperedges through the Lens of Category Theorycited by 0