trade-off-optimization

MemPilot is a framework that uses reinforcement learning to dynamically curate multimodal memory for LLM agents on demand, balancing performance, cost, and latency via adaptive retrieval and heterogeneous model selection. Experiments across five benchmarks show it achieves better performance-cost-latency trade-offs than existing trade-off-aware baselines.