MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents

TL;DR

MemPilot is a reinforcement learning-driven framework that dynamically manages multimodal memory for LLM agents by deciding when to use precomputed memory versus on-demand curation with specialized models. It adapts memory retrieval and processing based on performance, cost, and latency goals, enabling fine-grained control over computation allocation across heterogeneous models.

Why It Matters

It enables more efficient and adaptive memory use in multimodal agents, reducing unnecessary computation while preserving critical details. This improves scalability and responsiveness in real-world applications where resource constraints and task variability are significant.

Key Findings

• MemPilot uses a multi-step LLM policy optimized via reinforcement learning to make dynamic decisions about memory access and curation.
• It introduces objective-wise advantage decoupling and prefix-based marginal utility estimation for better credit assignment in multi-step decision processes.
• Experiments show MemPilot achieves superior trade-offs across performance, cost, and latency compared to existing methods, with broader Pareto frontiers.