MemPilot tunes on-demand multimodal memory for LLM agents
Original title: MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents
A reinforcement-learned policy chooses retrieval or query-specific curation under competing performance, cost, and latency goals.
MemPilot is a framework for on-demand memory curation in LLM agents that use multimodal history. Instead of always building query-agnostic memory, a multi-step policy learned with reinforcement learning chooses between retrieval and delegating query-specific curation to heterogeneous LLMs and VLMs. It controls evidence amount, instructions, model choice, and visual access. Objective-wise advantage decoupling and prefix-based marginal utility estimation handle competing goals and multi-step credit assignment. Tests on five benchmarks report broader trade-off frontiers than prior baselines.
Unconfirmed: Trade-off gains are reported by the authors and not independently confirmed here.
- The policy jointly controls evidence, instructions, model selection, and visual access.
- Curation can be delegated to heterogeneous LLMs and VLMs.
- Advantage estimates are computed per objective before aggregation.
- Prefix-based marginal utility assigns credit across multi-step rollouts.
- Evaluation covers five multimodal agent-memory benchmarks.
From the original article
1 min read at huggingface.co
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essential.
By Haozhen Zhang, Haodong Yue, Quanyu Long · huggingface.co
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