The Significance of D.I. Dubrovsky’s Information Theory of Consciousness for Designing Artificial Intelligence Architectures

The article explores the challenges of applying philosophical and neuroscientific theories of consciousness to artificial intelligence (AI) architectures, particularly large language models (LLMs). It argues that most of these theories were developed to explain biological consciousness and therefore cannot be seamlessly mapped onto artificial systems without risking oversimplified or unfounded analogies. This presents a conceptual challenge in the study of modern AI: while these systems demonstrate a capacity for complex “thinking,” there remains no compelling basis to attribute consciousness to them. To address this gap, the study adopts David Dubrovsky’s information theory of consciousness as its methodological foundation. Unlike classical philosophical approaches that postulate special entities to account for conscious perceptual experience, Dubrovsky’s framework offers a functional-informational account of subjective reality. Drawing on the principles of code dependence and substrate independence (the invariance of information relative to its physical carrier), the theory frames consciousness as the product of self-determination within a multilevel informational structure – an “ego-system.” This perspective provides a viable pathway for modeling consciousness on non-biological substrates. To operationalize these theoretical premises, the paper proposes two experimental architectures. The first aims to equip LLMs with an intrinsic or “self-context” – an autonomous, dynamic informational field that serves as a functional analogue to inner experience. Through interaction with the external environment and other agents, the model employs reinforcement learning to optimize this field, effectively emulating the ability to process “information about information.” The second architecture introduces a two-tiered cognitive system that separates continuous (sub-symbolic) computation from a discrete linguistic interface. By enabling controlled modifications, these designs allow researchers to formulate testable hypotheses and empirically investigate the mechanisms behind phenomena typically associated with consciousness, disentangling them from the machine’s baseline cognitive functions. The proposed approach does not purport to resolve the hard problem of consciousness. Rather, it highlights the substantial heuristic potential of Dubrovsky’s theory, whose principles of structural and functional organization of informational systems show a notable convergence with current trajectories in the development of advanced AI architectures.

1. Austin J., Johnson D.D., Ho J., Tarlow D., & van den Berg R. (2021) Structured Denoising Diffusion Models in Discrete State-Spaces. Advances in Neural Information Processing Systems. Vol. 34, pp. 17981–17993.

2. Baars B.J. (1988) A Cognitive Theory of Consciousness. Cambridge, UK: Cambridge University Press.

3. Clark A. (2015) Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford: Oxford University Press.

4. Dehaene S. & Changeux J.-P. (2011) Experimental and Theoretical Approaches to Conscious Processing. Neuron. Vol. 70, no. 2, pp. 200–227.

5. Dieleman S.E.L., Sartran L.P.M., Savinov N., Ganin I., Richemond P., Doucet A., …, & Hawthorne C.G.-M. (2024) Score Interpolation Diffusion Models (International Publication No. WO 2024/110596 A1). In: World Intellectual Property Organization. Retrieved from https://patents.google.com/patent/WO2024110596A1/en

6. Dubrovsky D.I. (1971) Mental Phenomena and the Brain: A Philosophical Analysis of the Problem in Connection with Some Current Tasks of Neurophysiology, Psychology, and Cybernetics. Moscow: Nauka (in Russian).

7. Dubrovsky D.I. (1980) Information, Consciousness, Brain. Moscow: Vysshaya shkola (in Russian).

8. Dubrovsky D.I. (2007) Consciousness, Brain, Artificial Intelligence. Moscow: Strategiya-Tsentr (in Russian).

9. Dubrovsky D.I. (2024a) The Problem of Mental Control: Neuroscience, Artificial Intelligence, Social Communications. Russian Journal of Philosophical Sciences = Filosofskie nauki. Vol. 67, no. 1, pp. 7–28 (in Russian).

10. Dubrovsky D.I. (2024b) Mind and Brain: Experience in Developing Fundamental Theoretical Issues of the Hard Problem of Consciousness and Its Significance for Neuroscience. Russian Journal of Philosophical Sciences = Filosofskie nauki. Vol. 67, no. 3, pp. 142–158 (in Russian).

11. Elamrani A. (2025) Introduction to Artificial Consciousness: History, Current Trends and Ethical Challenges. arXiv preprint. arXiv:2503.05823.

12. Gao Z., Chen L., Xiao Y., Xing H., Tao R., Luo H., Zhou J., & Dai B. (2025) Universal Reasoning Model. arXiv preprint. arXiv:2512.14693.

13. Lamme V.A.F. (2006) Towards a True Neural Stance on Consciousness. Trends in Cognitive Sciences. Vol. 10, no. 11, pp. 494–501.

14. Li J., Dong X., Zang Y., Cao Y., Wang J., & Lin D. (2025) Beyond Fixed: Variable-Length Denoising for Diffusion Large Language Models. arXiv preprint. arXiv:2508.00819.

15. Li M.Q. & Fung B. (2025) Security Concerns for Large Language Models: A Survey. arXiv preprint. arXiv:2505.18889.

16. Nedyak A.V. (2025) Is Dialogue with a Technosubject Possible? Architectures of Artificial Intelligence and Signatures of Consciousness. Russian Journal of Philosophical Sciences = Filosofskie nauki. Vol. 68, no. 3, pp. 93–113 (in Russian).

17. Nie S., Zhu F., You Z., Zhang X., Ou J., Zhou J., …, & Li C. (2025) Large Language Diffusion Models. arXiv preprint. arXiv:2502.09992.

18. Rosenthal D.M. (2005) Consciousness and Mind. Oxford: Clarendon Press.

19. Shiller A.V. & Petrunya O.E. (2021) Architectural Approach to Design of Emotional Intelligent Systems. Russian Journal of Philosophical Sciences = Filosofskie nauki. Vol. 64, no. 1, pp. 102–115.

20. Sohl-Dickstein J., Weiss E.A., Maheswaranathan N., & Ganguli S. (2015) Deep Unsupervised Learning Using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning. Vol. 37, pp. 2256–2265.

21. Song Y., Zhang Z., Luo C., Gao P., Xia F., Luo H., …, & Zhou H. (2025) Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference. arXiv preprint. arXiv:2508.02193.

22. Tononi G. (2008) Consciousness as Integrated Information: A Provisional Manifesto. Biological Bulletin. Vol. 215, no. 3, pp. 216–242.