Henry, Nathan INPedersen, MangorWilliams, MattMartin, Jamin LBDonkin, Liesje2025-10-092025-10-092025-10-06SN Computer Science, ISSN: 2661-8907 (Online), Springer Science and Business Media LLC, 6(7). doi: 10.1007/s42979-025-04369-42661-8907http://hdl.handle.net/10292/19920The value-loading problem is a major obstacle to creating Artificial Intelligence (AI) systems that align with human values and preferences. Central to this problem is the establishment of safe limits for repeatable AI behaviors. We introduce hormetic alignment, a paradigm to regulate the behavioral patterns of AI, grounded in the concept of hormesis, where low frequencies or repetitions of a behavior have beneficial effects, while high frequencies or repetitions are harmful. By modeling behaviors as allostatic opponent processes, we can use either Behavioral Frequency Response Analysis (BFRA) or Behavioral Count Response Analysis (BCRA) to quantify the safe and optimal limits of repeatable behaviors. We demonstrate how hormetic alignment solves the ‘paperclip maximizer’ scenario, a thought experiment where an unregulated AI tasked with making paperclips could end up converting all matter in the universe into paperclips. Our approach may be used to help create an evolving database of ‘values’ based on the hedonic calculus of repeatable behaviors with decreasing marginal utility. Hormetic alignment offers a principled solution to the value-loading problem for repeatable behaviors, augmenting current techniques by adding temporal constraints that reflect the diminishing returns of repeated actions. It further supports weak-to-strong generalization – using weaker models to supervise stronger ones – by providing a scalable value system that enables AI to learn and respect safe behavioral bounds. This paradigm opens new research avenues for developing computational value systems that govern not only single actions but the frequency and count of repeatable behaviors.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/46 Information and computing sciencesJournal ArticleOpenAccess10.1007/s42979-025-04369-4