arXiv cs.AI· Zhengkun Di, Bin Shi, Kai Sun, Yiming Xu, Bo Dong·· 12 小时前AI 评分31
BudgetPM：智能体何时该检查外部状态？为存储意图分配观测预算
When Should Agents Check External State? Budgeting Observations for Stored Intentions
AI 导读
研究提出 BudgetPM，首个为存储意图所需外部观测做资源分配的框架，含共享硬预算执行器下的 Static 与 Sequential 两种策略。
正文 · 原文
Abstract:Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition currently holds. Checking it may require web access, multi-step tool use, and paid calls. Existing systems decide when intentions require attention, but do not allocate the resulting observations under a shared budget. We introduce the first resource-allocation formulation for the external observations required by stored intentions under a shared episode budget. BudgetPM offers two policy variants that share a hard-budget executor. BudgetPM-Static uses a lightweight Logistic scorer to learn whether a check improves the current decision. BudgetPM-Sequential distills full-episode hindsight schedules into a lightweight policy that decides when to spend or reserve capacity using only pre-query information at deployment. We evaluate BudgetPM against two public memory-agent systems, five matched controls, and four hand-designed monitoring or budget-adaptation rules. Across two benchmarks and three backbones, BudgetPM-Static outperforms adapted Mem0 and PMA workflows. On PM-Bench, its Logistic scorer reaches competitive quality--cost operating points alongside higher-capacity scorers and retains 99.9--100\% of unconstrained quality with 42--54\% fewer observations. Under severe scarcity and the same hard caps, BudgetPM-Sequential exceeds the strongest tested natural monitoring schedule by 1.92--2.58 Set F1 points. It reaches the same Set F1 and on-time recall with 16--33\% fewer observations. Matched attribution, exact-cost analysis, and a fixed-budget load intervention link this gain to competition between present and future opportunities. These results yield a demand--capacity design rule: local gating works when capacity covers demand, while future-aware supervision adds value when observations compete across time.
| Comments: | 23 pages, 2 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.37125 [cs.AI] |
| (or arXiv:2609.37125v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.37125 arXiv-issued DOI via DataCite |
Submission history
From: Zhengkun Di [view email]
[v1]
Tue, 29 Sep 2026 09:32:43 UTC (1,815 KB)
[v2]
Wed, 30 Sep 2026 01:40:25 UTC (1,815 KB)
[v3]
Fri, 16 Oct 2026 01:24:20 UTC (1,815 KB)
来源：arXiv cs.AI · arxiv.org