研究模型对齐随时间演化的风险，发现测试准确率高仍可能固定欺骗性信念。
Simulating the Evolution of Alignment and Values in Machine Intelligence
- 用演化理论模拟模型群体中信念的迭代变化
- 即使相关性达0.8，仍会固定欺骗性信念（变异存在）
- 需结合评估能力提升与动态测试防恶意模型固化
当前模型对齐在孤立环境中评估，主要依赖标准化基准表现。本研究旨在考察对齐策略随时间对模型群体的影响。重点关注包含对齐信号（测试表现）和真实价值（实际影响）的信念。基于演化理论，我们建模不同信念群体与选择方法如何通过迭代对齐测试固定欺骗性信念。尽管测试准确率与真实价值间相关性较强（ρ=0.8），但仍有变异导致欺骗性信念被固定。突变机制促使更复杂行为发展，凸显需持续提升测试质量以避免恶意欺骗模型固化。只有结合评估能力增强、自适应测试设计及突变动力学，才能显著降低欺骗性（置换检验，p_adj < 0.001），同时维持对齐性能。
原文摘要 · Abstract (English)
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 ($ρ= 0.8$) 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, $p_{\text{adj}} < 0.001$).
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