Categorical AI phenomenology: A first-person approach

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-person structures, modeled mathematically by categories derived from Q-networks to capture actions and phenomenological invariants. In this framework, Q-networks are conceptualized as relational interfaces encoding agent-world interaction, analogous to how the dynamical states of a computer depend on its sensory inputs, previous states, and actions. Our work provides a rigorous framework for interface consciousness to describe computational systems that embed information-processing into phenomenological structure. The approach aligns with 4E approaches to cognition by emphasizing enactive, embedded, and extended dimensions of experience. The paper thus offers a principled, relational, and phenomenological account of artificial phenomenology grounded in categorical mathematics.

Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)

arXiv:2608.20420 [cs.AI]

(or arXiv:2608.20420v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.20420 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

R. Prentner (2026). Categorical AI Phenomenology: A First-Person Approach, Journal of Artificial Intelligence and Consciousness

https://doi.org/10.1142/S2705078526500013 Focus to learn more DOI(s) linking to related resources

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