arXiv:cs.AI· Yulong Ming, Jie Xu, Zihan Wu, Xiaohua Jia·· 5 小时前AI 评分42
PACE:何时将 Computer-Use Agent 编译为程序?衡量回报与编译决策以优化 Token 效率
When to Compile a Computer-Use Agent? Measuring Payback and Making Compilation Decisions for Token Efficiency
AI 导读
研究者提出 PACE(Payback-Aware Compilation from Experience),通过测量协议记录编译成功与失败成本、对比 agent 与程序执行成本来估算每次使用节省量和回报次数,并据此在线决定是否编译。
正文
Abstract:Compiling GUI procedures that agents execute repeatedly into programs can reduce their token costs. However, measuring payback and deciding when to compile have two challenges. First, compilation costs are uncertain because attempts can require repair and still fail to produce a usable program. Second, future reuse is unknown because tasks may stop arriving or GUI drift may stop the program from working. To address these challenges, we propose PACE (Payback-Aware Compilation from Experience), a system with a measurement protocol and an online compilation algorithm. The measurement protocol records successful and failed compilation costs, and compares agent and program execution costs on matched task inputs to estimate per-use savings and payback counts. Using these measurements, the online algorithm compares estimated future savings with compilation costs, including failed attempts, based on past task arrivals and compilation outcomes. It checks execution and compilation charges against a cumulative budget determined by observed task arrivals before allowing either action. Under stated action-cost assumptions, total cost after each arrival is at most $1+\epsilon$ times the cost of running every task with the agent. For successful compilation attempts, estimated payback counts excluding source agent runs are 2-16 uses. In simulations using recorded task arrivals, PACE reduces token costs by 17.3% compared with ReAct, 24.9% with the AutoRPA adaptation, and 17.3% with the ToolPro adaptation on average ($\epsilon=0.25$).
| Comments: | 27 pages, 3 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02932 [cs.AI] |
| (or arXiv:2610.02932v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02932 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yulong Ming [view email]
[v1]
Fri, 2 Oct 2026 07:22:46 UTC (290 KB)
来源:arXiv:cs.AI · arxiv.org