Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory

Haozhen Zhang; Haodong Yue; Tao Feng; Quanyu Long; Jianzhu Bao; Bowen Jin; Weizhi Zhang; Xiao Li; Jiaxuan You; Chengwei Qin; Wenya Wang

arXiv · 2602.06025

Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory

Haozhen Zhang·Haodong Yue·Tao Feng·Quanyu Long·Jianzhu Bao·Bowen Jin·Weizhi Zhang·Xiao Li·Jiaxuan You·Chengwei Qin·Wenya Wang

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Abstract

Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can be inefficient and may discard query-critical information. Although runtime memory utilization is a natural alternative, prior work often incurs substantial overhead and offers limited explicit control over the performance-cost trade-off. In this work, we present \textbf{BudgetMem}, a runtime agent memory framework for explicit, query-aware performance-cost control. BudgetMem structures memory processing as a set of memory modules, each offered in three budget tiers (i.e., \textsc{Low}/\textsc{Mid}/\textsc{High}). A lightweight router performs budget-tier routing across modules to balance task performance and memory construction cost, which is implemented as a compact neural policy trained with reinforcement learning. Using BudgetMem as a unified testbed, we study three complementary strategies for realizing budget tiers: implementation (method complexity), reasoning (inference behavior), and capacity (module model size). Across LoCoMo, LongMemEval, and HotpotQA, BudgetMem surpasses strong baselines when performance is prioritized (i.e., high-budget setting), and delivers better accuracy-cost frontiers under tighter budgets. Moreover, our analysis disentangles the strengths and weaknesses of different tiering strategies, clarifying when each axis delivers the most favorable trade-offs under varying budget regimes.

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BibTeX RIS

Haozhen Zhang, Haodong Yue, Tao Feng, Quanyu Long, Jianzhu Bao, Bowen Jin, Weizhi Zhang, Xiao Li, Jiaxuan You, Chengwei Qin, Wenya Wang. 2026-05-27. Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory. https://arxiv.org/abs/2602.06025

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