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

1. 1Design a multi-user scenario with changing preferences and habits
2. 2Implement a long-term memory mechanism in the in-vehicle agent
3. 3Evaluate the agent's performance using VehicleMemBench
4. 4Analyze the results to identify areas for improvement

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

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

🚗💡 VehicleMemBench: a new benchmark for multi-user long-term memory in in-vehicle agents