Agent Behavior Maps：不跑环境也能定位智能体行为差异
Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
arXiv 上这篇论文给智能体行为画了张“地图”，不用实际跑任务就能比较 100 个 LLM 的行为差异，做 agent 评测的可以看看。
论文提出 Agent Behavior Map，用共享参考轨迹上的中心化动作对数似然来表示 LLM-harness 组合，无需为目标智能体跑新的环境 rollout。作者推导了坐标距离与轨迹 KL 散度之间误差界的理论关系，并在 8 个交叉评分模型上验证距离排名与实测轨迹 KL 一致。在 100 个 LLM 和 4 个环境的实验中，基于 ALFWorld 的坐标可用于性能预测和识别行为相似性，且这些相似性在控制成功率和模型规模后依然存在。作为扩展，harness 组件的位移近似可叠加，但精度随组件和环境不同而变化。
Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference trajectories, without requiring new environment rollouts from the target agent. We derive error bounds relating squared coordinate distances to trajectory KL under explicit assumptions, and across eight cross-scored models the distances track the directly measured trajectory KL in rank order. Across 100 LLMs and four environments, ALFWorld-based coordinates support performance prediction and identify behavioral similarities. In ALFWorld, these similarities persist after controlling for success rates and model size, including across model families. As an extension, harness-component displacements approximately compose, with accuracy varying across components and environments.
