CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty
This addresses the need for more reliable and self-aware LLM agents in real-world settings, such as in-car voice assistants, by providing a benchmark to evaluate their performance under uncertainty, though it is incremental as it builds on existing benchmarks by adding new task types.
The paper tackles the problem of evaluating LLM agents' reliability in real-world, user-facing applications like in-car assistants, where uncertainty arises from incomplete or ambiguous requests, and introduces CAR-bench to assess consistency, uncertainty handling, and limit-awareness, with baseline results showing frontier LLMs achieve less than 50% consistent pass rate on Disambiguation tasks and frequently violate policies in Hallucination tasks.
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.