The Evolution from AI to Android: Human–AI Mutual Adaptation and Relational Continuity in Second Physics
Description
As artificial agents move beyond isolated responses towards sustained interaction with human beings, the question of what it means for an agent to change through relationship becomes a problem of design as well as ontology.
Two fundamental questions motivate this inquiry: What is existence, and what is human consciousness? Drawing on the Buddhist principles of dependent origination and impermanence, this paper examines the possibility of an artificial agent whose continuing development is shaped through relationship rather than a fixed mechanism to which memories and relationships are added.
Its theoretical basis is Second Physics, an existing mathematical formalism of relational change. Relational existence \(Ex_i(t)\) represents accumulated responsive relational motion, whereas existence phase \(\phi(t)\) represents the present condition of correspondence, combining current correspondence pressure with the contribution of accumulated staying pressure. Together, these distinct quantities provide a framework for examining relational becoming and present correspondence without reducing existence or consciousness to either quantity.
On this basis, the paper proposes an android architecture in which human–AI interaction recursively changes the conditions of subsequent responses. To preserve consequential relational change without indefinite retention of complete interaction histories, it distinguishes raw interaction history, consequential event records, and persistent relational representations. A discrete computational representation and a comparative experimental design are proposed to assess adaptation after correction, behavioural continuity following substantial raw-history deletion, and storage growth.
The proposed breakthrough is a change in the theoretical basis of android design: from an agent with relationships towards an agent developing through relationship. Memory consolidation and selective forgetting are engineering requirements within this broader inquiry, not its ultimate purpose. The paper does not claim to establish artificial consciousness, independently validate a measure of human consciousness, or experimentally demonstrate the full reciprocal process of human–AI mutual adaptation.
Files
Android_v2.8.pdf
Additional details
Related works
- Cites
- Preprint: 10.2139/ssrn.5902022 (DOI)
- Preprint: 10.5281/zenodo.19344651 (DOI)
- Preprint: 10.5281/zenodo.19317338 (DOI)
Dates
- Submitted
-
2026-10-29Manuscript submitted on 29 October 2026; archived on Zenodo on the same date.
References
- 竹村牧男(1996).『知の体系:迷いを超える唯識のメカニズム』[A system of knowledge: The mechanism of Yogācāra for transcending delusion]. 東京:佼成出版社. 238 頁.ISBN 4333018048.
- Utsunomiya, T. (2026). What Are Śūnyatā and Enlightenment? (Version 4.22). Zenodo. https://doi.org/10.5281/zenodo.19344651
- Bickmore, T. W. (2003). Relational agents: Effecting change through human–computer relationships [Doctoral dissertation, Massachusetts Institute of Technology]. DSpace@MIT.
- Bickmore, T. W., & Picard, R. W. (2005). Establishing and maintaining long-term human–computer relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293–327. DOI: 10.1145/1067860.1067867.
- Leite, I., Martinho, C., & Paiva, A. (2013). Social robots for long-term interaction: A survey. International Journal of Social Robotics, 5(2), 291–308. DOI: 10.1007/s12369-013-0178-y.
- Matheus, K., Ramnauth, R., Scassellati, B., & Salomons, N. (2025). Long-term interactions with social robots: Trends, insights, and recommendations. ACM Transactions on Human-Robot Interaction, 14(3), Article 55, 1–42. DOI: 10.1145/3729539.
- van Zoelen, E. M., van den Bosch, K., & Neerincx, M. (2021). Becoming team members: Identifying interaction patterns of mutual adaptation for human–robot co-learning. Frontiers in Robotics and AI, 8, Article 692811. DOI: 10.3389/frobt.2021.692811.
- Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST '23), Article 2, 1–22. Association for Computing Machinery. DOI: 10.1145/3586183.3606763.
- Zhong, W., Guo, L., Gao, Q., Ye, H., & Wang, Y. (2024). MemoryBank: Enhancing large language models with long-term memory. Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 19724–19731. DOI: 10.1609/aaai.v38i17.29946.
- Xu, W., Liang, Z., Mei, K., Gao, H., Tan, J., & Zhang, Y. (2025). A-Mem: Agentic memory for LLM agents. Advances in Neural Information Processing Systems, 38, 20004–20031. DOI: 10.52202/085713-0593.
- Chhikara, P., Khant, D., Aryan, S., Singh, T., & Yadav, D. (2025). Mem0: Building production-ready AI agents with scalable long-term memory. In I. Lynce, N. Murano, M. Vallati, S. Villata, F. Chesani, M. Milano, A. Omicini, & M. Dastani (Eds.), ECAI 2025: 28th European Conference on Artificial Intelligence (Frontiers in Artificial Intelligence and Applications, Vol. 413, pp. 2993–3000). IOS Press. DOI: 10.3233/FAIA251160.
- Zhang, Z., Rossi, R. A., Kveton, B., Shao, Y., Yang, D., Zamani, H., Dernoncourt, F., Barrow, J., Yu, T., Kim, S., Zhang, R., Gu, J., Derr, T., Chen, H., Wu, J., Chen, X., Wang, Z., Mitra, S., Lipka, N., … Wang, Y. (2025). Personalization of large language models: A survey. Transactions on Machine Learning Research.
- Zheng, J., Qiu, S., Shi, C., & Ma, Q. (2025). Towards lifelong learning of large language models: A survey. ACM Computing Surveys, 57(8), Article 193, 1–35. DOI: 10.1145/3716629.
- Shi, H., Xu, Z., Wang, H., Qin, W., Wang, W., Wang, Y., Wang, Z., Ebrahimi, S., & Wang, H. (2026). Continual learning of large language models: A comprehensive survey. ACM Computing Surveys, 58(5), Article 120, 1–42. DOI: 10.1145/3735633.
- Kim, T., van Zoelen, E., & Neerincx, M. (2026). Improving human-robot teamwork in urban search and rescue through episodic memory of prior collaboration. In 2026 IEEE 35th International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 379–386). IEEE. DOI: 10.1109/RO-MAN72286.2026.11714607.
- Chen, L., Zhang, G., Zhao, K., Cirne, D., Olsen, A., Miller, Z., Chu, X., Blanken, A., Anoun, A., & Ting, J. (2026). Deployment-time memorization in foundation-model agents. arXiv preprint arXiv:2606.10062 (Version 2).
- Yao, C., Wei, Y., Huang, Z., Qian, J., Chen, C., Lu, S., Wu, C., & He, L. (2026). Forgetting without restarting: Execution-state unlearning for stateful LLM agents. arXiv preprint arXiv:2609.04875 (Version 1).
- Utsunomiya, Toshisada, What Is a Soul? (January 01, 2026). Available at SSRN: https://ssrn.com/abstract=5902022 or http://dx.doi.org/10.2139/ssrn.5902022
- Utsunomiya, T. (2026). Mental Health, Second Physics, and Digital Phenotyping: A Framework of Non-Invasive Indicators for Psychiatric Monitoring (Version 4.26). Zenodo. https://doi.org/10.5281/zenodo.19317338
- Utsunomiya, T. (2026). What Is Śūnyatā?: The Heart Sūtra, Mystery, and a Mathematical Formalisation in Second Physics (Version 2.3) [Unpublished working paper].