Problem
Research questions and friction points this paper is trying to address.
Lifelong Learning
Inference-Time Memory Allocation
Resource Optimization
Experience Replay
LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents
🤖 AI Summary
论文提出LIMBO框架，通过在线学习动态优化LLM代理在推理时的记忆使用和计算资源分配，以解决终身学习中资源有效利用的问题。
📝 Abstract
As LLM agents become integrated into increasingly complex workflows, they must continually acquire new capabilities while retaining competence on previously learned tasks. Lifelong agents address this through experience replay, injecting past interactions into the prompt to leverage prior experience during inference. However, replay is not free: every replayed trajectory competes with retrieval, reasoning, tool use, and verification for the same limited prompt and compute budget, making effective resource allocation essential. Existing approaches allocate these resources using fixed replay policies, regardless of whether replay is beneficial for the current task. We identify this as inference-time memory allocation, a distinct problem class for lifelong agents, and introduce LIMBO: the first online framework to our knowledge that treats memory as a controllable inference-time resource and jointly optimizes memory strategy and inference budget for each incoming task. Unlike prior approaches that fix the replay policy or require model weights, teacher supervision, or offline retraining, LIMBO learns this allocation online in a single pass, explicitly balancing task performance and inference cost without modifying the underlying agent. Across three LLM backbones on LifelongAgentBench, LIMBO achieves better cost-accuracy tradeoffs than state-of-the-art memory-augmented baselines and nearly matches all strongest such baselines at up to ~83% lower inference cost (~53% on average). LIMBO adapts its policy across models and environments without retraining, demonstrating that effective allocation can be learned online rather than manually specified.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Lifelong Inference-Time Memory
Budget Optimization
Online Learning
Memory Strategy
Inference Cost
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