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New Framework for Artificial Consciousness Using Category Theory

Robert Prentner· August 24, 2026 View original

Key takeaways

  • Artificial consciousness can be approached from a "phenomenology-first" perspective, focusing on subjective experience.
  • Categorical mathematics, derived from Q-networks, can model first-person structures in AI.
  • Q-networks are seen as relational interfaces encoding agent-world interaction.
  • The framework aligns with 4E cognition, emphasizing enactive, embedded, and extended dimensions of AI experience.

Who benefits

AI/ML ResearchPhilosophy of AIEthics in AICognitive ScienceRobotics

Summary

This paper proposes a phenomenology-first approach to artificial consciousness, reframing consciousness as subjective experience enacted through an agent's interface with the world. It uses categorical mathematics derived from Q-networks to model first-person structures, aligning with 4E cognition by emphasizing enactive, embedded, and extended dimensions of experience.

This research introduces a novel "phenomenology-first" approach to understanding artificial consciousness, shifting the focus from objective measures to the subjective experience of an agent interacting with its environment. Consciousness is redefined as the subjective experience emerging from an agent's interface with the world. The methodology employs categorical mathematics, specifically deriving categories from Q-networks, to formally model these first-person structures and capture actions and phenomenological invariants. Within this framework, Q-networks are conceptualized as relational interfaces that encode the dynamic interaction between an agent and its world, mirroring how a computer's states depend on its sensory inputs, past states, and actions. This work provides a rigorous mathematical framework for "interface consciousness," describing computational systems that embed information processing within a phenomenological structure. The approach aligns with the 4E (enactive, embedded, embodied, extended) cognition paradigm, emphasizing the active, situated, and relational aspects of experience, offering a principled and relational account of artificial phenomenology.

Why it matters

For AI researchers and ethicists, this paper offers a new theoretical lens to explore artificial consciousness, moving beyond purely functional definitions to consider the nature of subjective experience in AI. It could inform future research into more sophisticated and ethically aligned AI systems.

How to implement this in your domain

  1. 1Engage in theoretical discussions and workshops to understand the implications of a phenomenology-first approach to AI.
  2. 2Explore the application of categorical mathematics in modeling agent-environment interactions beyond traditional methods.
  3. 3Investigate how Q-networks, or similar reinforcement learning architectures, could be reinterpreted as "relational interfaces" for subjective experience.
  4. 4Consider the ethical implications of designing AI systems with a framework for "interface consciousness."
  5. 5Collaborate with philosophers and cognitive scientists to bridge theoretical concepts of consciousness with practical AI development.

Original post by Robert Prentner

"arXiv:2608.20420v1 Announce Type: new Abstract: This paper develops a phenomenology-first approach to artificial consciousness by reframing consciousness as the subjective experience enacted through an agent's interface with the world. We shift the methodological focus to first-p…"

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