Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. In domains, such as in-car voice assistants, users often issue incomplete or ambiguous requests, creating intrinsic uncertainty that agents must manage through dialogue, tool use, and policy adherence. We introduce CAR-bench, a benchmark for evaluating consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. The environment features an LLM-simulated user, domain policies, and 58 interconnected tools spanning navigation, productivity, charging, and vehicle control. Beyond standard task completion, CAR-bench introduces Hallucination tasks that test agents' limit-awareness under missing tools or information, and Disambiguation tasks that require resolving uncertainty through clarification or internal information gathering. Baseline results reveal large gaps between occasional and consistent success on all task types. Even frontier reasoning LLMs achieve less than 50% consistent pass rate on Disambiguation tasks due to premature actions, and frequently violate policies or fabricate information to satisfy user requests in Hallucination tasks, underscoring the need for more reliable and self-aware LLM agents in real-world settings.

翻译：现有的大型语言模型（LLM）智能体基准测试主要关注理想化环境下的任务完成度，却忽视了其在面向用户的现实世界应用中的可靠性。在诸如车载语音助手等场景中，用户常提出不完整或模糊的请求，从而产生内在的不确定性，智能体必须通过对话、工具使用和策略遵循来应对这种不确定性。我们提出了CAR-bench，这是一个用于评估车载助手领域中多轮次、使用工具的LLM智能体在一致性、不确定性处理及能力认知方面的基准测试。该环境包含一个由LLM模拟的用户、领域策略以及涵盖导航、生产力、充电和车辆控制等功能的58个相互关联的工具。除标准任务完成度外，CAR-bench引入了“幻觉任务”，用于测试智能体在工具或信息缺失时的能力边界认知；以及“消歧任务”，要求智能体通过澄清或内部信息收集来化解不确定性。基线结果显示，所有任务类型上偶尔成功与持续成功之间存在巨大差距。即使在最前沿的推理型LLM上，由于过早采取行动，其在消歧任务中的持续通过率也不足50%；在幻觉任务中，它们经常违反策略或捏造信息以满足用户请求，这凸显了现实世界场景中对更可靠、更具自我认知能力的LLM智能体的迫切需求。