VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

📰 ArXiv cs.AI

VehicleMemBench is a benchmark for evaluating multi-user long-term memory in in-vehicle agents

advanced Published 26 Mar 2026

Action Steps

Design a multi-user scenario with changing preferences and habits
Implement a long-term memory mechanism in the in-vehicle agent
Evaluate the agent's performance using VehicleMemBench
Analyze the results to identify areas for improvement

Who Needs to Know This

AI engineers and researchers designing in-vehicle agents can benefit from this benchmark to evaluate their models' ability to handle multi-user preferences and conflicts

Key Insight

💡 Existing benchmarks are insufficient for evaluating in-vehicle agents' ability to handle multi-user preferences and conflicts

Key Takeaways

VehicleMemBench is a benchmark for evaluating multi-user long-term memory in in-vehicle agents

Full Article

Title: VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

Abstract:
arXiv:2603.23840v1 Announce Type: new Abstract: With the growing demand for intelligent in-vehicle experiences, vehicle-based agents are evolving from simple assistants to long-term companions. This evolution requires agents to continuously model multi-user preferences and make reliable decisions in the face of inter-user preference conflicts and changing habits over time. However, existing benchmarks are largely limited to single-user, static question-answer settings, failing to capture the tem

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