Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
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{M...
| Authors | Haozhen Zhang, Haodong Yue, Tao Feng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 February 2026 |
| Last updated at source | 27 May 2026 |
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