MemPilot: RL-orchestrated on-demand multimodal memory for LLM agents
NanyangTechnologicalUniversity · hf · 2026-10-20
A paper from NTU, Tsinghua and UIUC, MemPilot argues most agent memory systems build memory query-agnostically, wasting preprocessing cost and discarding details that later prove essential.
- Optimizes a multi-step LLM policy via reinforcement learning to iteratively choose between retrieving from existing memory and delegating query-specific curation of raw multimodal history
- Balances performance, cost and latency through adaptive retrieval and heterogeneous memory-model selection
- Project page and code released
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