Simulating the Evolution of Alignment and Values in Machine Intelligence

Computer Science > Artificial Intelligence [Submitted on 7 Apr 2026] Simulating the Evolution of Alignment and Values in Machine Intelligence Jonathan Elsworth Eicher Model alignment is currently applied in a vacuum, evaluated primarily through standardised benchmark performance. The purpose of this study is to examine the effects of alignment on populations of models through time. We focus on the treatment of beliefs which contain both an alignment signal (how well it does on the test) and a true value (what the impact actually will be). By applying evolutionary theory we can model how different populations of beliefs and selection methodologies can fix deceptive beliefs through iterative alignment testing. The correlation between testing accuracy and true value remains a strong feature, but even at high correlations (\rho = 0.8) there is variability in the resulting deceptive beliefs that become fixed. Mutations allow for more complex developments, highlighting the increasing need to update the quality of tests to avoid fixation of maliciously deceptive models. Only by combining improving evaluator capabilities, adaptive test design, and mutational dynamics do we see significant reductions in deception while maintaining alignment fitness (permutation test, p_{\text{adj}} < 0.001). Comments: 9 pages, 7 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.05274 [cs.AI]   (or arXiv:2604.05274v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.05274 Focus to learn more Submission history From: Jonathan Eicher [view email] [v1] Tue, 7 Apr 2026 00:18:28 UTC (4,802 KB) Access Paper: HTML (experimental) view license Ancillary files (details): supplementary_information.pdf Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)