TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents
Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability but on consistency and limit-awareness: behaving the same way across repeated trials, and recognizing when a request cannot, or cannot yet, be safely fulfilled. CAR-bench exposes this reliability gap in the domain of in-car assistants: an LLM-simulated user issues incomplete or ambiguous requests, requiring the agent to resolve uncertainty through multi-turn dialogue and tool use while strictly adhering to domain policies. Even frontier models show a substantial gap between what they can solve at least once (Pass@3) and what they solve consistently across trials (Pass^k). We bridge this gap w...
| Authors | Wenhao Wu, Menghao Zhang, Xin Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 August 2026 |
| Last updated at source | 3 September 2026 |
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