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Research questionHow can in-car LLM agents respond consistently to incomplete requests they cannot safely fulfill?An agent may succeed on a request occasionally while failing on repeated attempts or proceeding when it should clarify, defer, or refuse. In-car assistance makes this difficult because user intent is often incomplete and responses may depend on stateful dialogue and tool use.
AI Agents
Alignment & Safety
Evaluation & Benchmarks
LLM Pretraining & Post-training
Latest papersRecent research connected to this question, newest first.TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM AgentsThe evidence comes from CAR-bench, where an LLM-simulated user issues incomplete or ambiguous requests to an in-car assistant operating under domain policies. Reported results concern skill-based agents evaluated across repeated trials, including GPT-5.5 and GPT-5.6-Sol systems; the input does not establish broader deployment capabilities beyond this setting.research paper · Sep 3, 2026
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