这项研究解决了自适应智能体行为难以量化追踪的痛点，做AI安全、智能体行为分析的团队可以直接用这套方法评估模型特质变化，值得关注。
该研究提出了一种通过分析技能文件、记忆文件等文本编辑来测量智能体特质的方法。研究者将特质定义为文本嵌入空间中的方向，通过训练线性模型学习特质向量，并利用嵌入差异投影来评分任意技能编辑。在68个标注数据上，该方法对敏感数据获取倾向特质的符号分类准确率达91.2%，斯皮尔曼秩相关系数为0.82。该框架还支持智能体间通过可信中介评估技能文件更新，为自适应智能体的行为监控提供了新工具。
Tracking the Behavioral Trajectories of Adapting Agents
Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act. Through edits by humans or the agents themselves, these files may evolve over time, directly steering the agent's behavior in future interactions. We present a methodology and framework for measuring agent $traits$ by defining traits as directions in the embedding space of a text embedding model. We train a linear model on labeled "before" versus "after" skill file diffs to learn a trait vector, then score arbitrary skill edits by projecting their embedding diffs onto this vector. Evaluated on 68 labeled skill diff pairs for the trait of propensity to seek sensitive data, our method achieves 91.2% sign classification accuracy and a Spearman rank correlation of $ρ= 0.82$ under leave-one-out cross-validation. We build this trait evaluation into a broader agent-to-agent protocol that enables one agent to evaluate another's skill file updates through a trusted intermediary.