AgentHabit：刻画智能体在日常任务中的不同行为
AgentHabit: Characterizing Distinct Behaviors of Agents on Everyday Tasks
AI总结：
提出HABIT分类体系和AgentHABIT基准，通过23个行为轴刻画LLM智能体在日常任务中的行为倾向，揭示模型间差异并支持行为调控。
AI中文摘要：
大语言模型（LLM）智能体协助用户完成许多可以以多种合理方式执行的日常任务。即使它们的答案是有用的，智能体执行这些任务的方式也可能不符合用户的偏好和需求。例如，智能体在是否提出澄清性问题或是否搜索网络方面存在差异。我们引入了HABIT，一个包含五个类别中23个行为轴的分类体系，由三位作者和三个LLM从跨越17个领域的408条智能体轨迹中自下而上地推导得出。在保留任务上，HABIT比现有的人类价值观和智能体行为分类体系更清晰地区分模型，同时支持相当一致的标注。基于HABIT，我们构建了AgentHABIT，一个基准测试，通过智能体在86个日常任务上的轨迹来刻画每个智能体的行为倾向。使用AgentHABIT对18个模型进行刻画，揭示了各种独特的倾向。例如，大多数GPT和Claude模型会陈述其假设并在需求冲突时提供替代方案，而Qwen和Google的模型则更经常不说明假设或需求的变化。即使从完全不同的任务集构建，这些特征仍然可识别，表明它们反映的是普遍倾向而非特定任务行为。提示智能体采用特定行为可以轻易改变某些轴，但几乎不改变其他轴，而在另一个模型的轨迹上进行微调只会改变模型特征的一部分，其余大部分保持不变。总体而言，HABIT和AgentHABIT提供了一个系统框架，用于刻画智能体如何执行日常任务，超越任务成功本身，为开发行为更符合用户需求的智能体提供见解。
英文摘要：
Large language model (LLM) agents assist users with everyday tasks that can be completed in many reasonable ways. Even when their answers are useful, how agents carry out these tasks may not match users' preferences and needs. For example, agents differ in whether they ask clarifying questions or search the web. We introduce HABIT, a taxonomy of 23 behavioral axes in five categories, which three authors and three LLMs derive bottom-up from 408 agent trajectories across 17 domains. On held-out tasks, HABIT distinguishes models more clearly than existing taxonomies of human values and agent actions while supporting comparably consistent annotation. Building on HABIT, we construct AgentHABIT, a benchmark that profiles each agent's behavioral tendencies from its trajectories on 86 everyday tasks. Profiling 18 models with AgentHABIT reveals a range of distinctive tendencies. For example, most GPT and Claude models state their assumptions and offer alternatives when requirements conflict, whereas Qwen and Google's models more often leave assumptions or changes to requirements unstated. These profiles remain recognizable even when built from entirely different sets of tasks, indicating that they reflect general tendencies rather than task-specific behavior. Prompting agents to adopt specific behaviors shifts some axes readily but barely changes others, while fine-tuning on another model's trajectories changes only part of a model's profile and leaves much of it intact. Overall, HABIT and AgentHABIT provide a systematic framework for characterizing how agents carry out everyday tasks beyond task success, offering insights to guide the development of agents whose behavior better fits users' needs.
