Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
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| Format: | Preprint |
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2026
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| _version_ | 1866914608187441152 |
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| author | Zhang, Haozhen Yue, Haodong Feng, Tao Long, Quanyu Bao, Jianzhu Jin, Bowen Zhang, Weizhi Li, Xiao You, Jiaxuan Qin, Chengwei Wang, Wenya |
| author_facet | Zhang, Haozhen Yue, Haodong Feng, Tao Long, Quanyu Bao, Jianzhu Jin, Bowen Zhang, Weizhi Li, Xiao You, Jiaxuan Qin, Chengwei Wang, Wenya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory Zhang, Haozhen Yue, Haodong Feng, Tao Long, Quanyu Bao, Jianzhu Jin, Bowen Zhang, Weizhi Li, Xiao You, Jiaxuan Qin, Chengwei Wang, Wenya Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.06025 |