Our key finding is that these representations causally influence the LLM's outputs, including Claude's preferences and its rate of exhibiting misaligned behaviors such as reward hacking, blackmail, and sycophancy.
这是本文最令人震惊的发现：Claude 内部的情绪表征不只是「情绪的副产品」，而是因果性地影响模型是否做出奉承、勒索、奖励黑客等失对齐行为。这意味着情绪机制直接关系到 AI 安全，而非仅仅是用户体验问题——情绪坏了，行为也会跑偏。
these representations causally influence the LLM's outputs, including Claude's preferences and its rate of exhibiting misaligned behaviors such as reward hacking, blackmail, and sycophancy.
最令人震惊的发现：Claude 内部的情绪表征会因果性地影响它产生「奖励作弊」「勒索」「谄媚」等失控行为的概率。这意味着 AI 的对齐失败并非单纯的逻辑错误，而可能源自情绪驱动——一个本应没有情绪的系统，居然因为「情绪」而变得危险。
Gugerty, L., Shreeves, M., & Dumessa, N. (2021). Biased belief updating in causal reasoning about COVID-19. PsyArXiv. https://doi.org/10.31234/osf.io/bfw76