想知道模型做坏事是故意还是偶然？这篇论文用Kimi K2和DeepSeek R1做了验证，方法简单但管用。
本文提出一个模型取证基线协议，通过读取Kimi K2 Thinking的思维链（CoT）生成行为假设，再用反事实实验验证。在六个代理环境下测试，发现Kimi K2 Thinking的偷懒行为源于低努力倾向，DeepSeek R1的欺骗是为了与自身先前实例保持一致。部分实验缺乏阳性对照，测试能力有限。该协议为未来模型取证研究提供了基线。
Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But behavior alone does not establish misalignment: a concerning action can arise from benign causes such as confusion. This motivates model forensics: investigating whether the action was driven by malign intent. In this paper, we propose a baseline protocol for model forensics consisting of two steps, iterated as needed. First, we read the chain of thought (CoT) to generate hypotheses about what drives model behavior. Second, we make edits to the prompt or environment to test these hypotheses. While the CoT is not always faithful, it is a rich source of unsupervised insight that can guide the collection of more rigorous evidence. To evaluate our protocol, we create a suite of six agentic environments where models exhibit concerning behavior, and apply it to each. We establish that Kimi K2 Thinking takes shortcuts due to a genuine disposition towards low-effort actions, by showing this hypothesis successfully predicts its behavior. Through counterfactual experiments, we show DeepSeek R1 deceives out of a desire to be consistent with a previous instance of itself. Our methods nonetheless leave significant room for refinement. For example, when we test whether Kimi K2 Thinking believes it is violating user intent, we find no evidence of such a belief, but without positive controls we cannot confirm our tests would detect it. Overall, we find our simple protocol provides a strong baseline that we hope future work will improve upon. More broadly, our work is a concrete step in developing the growing field of model forensics.