这篇论文教你如何让AI代理少花钱多办事，用CAM-DF方法在成本压力下精准选择工具，比传统方法更高效。
该论文提出CAM-DF（成本感知边际决策聚焦停止）方法，解决LLM代理在工具选择时如何平衡信息充足性与成本的问题。CAM-DF通过训练停止决策，在1,343个任务上评估，在τ-bench Retail数据集上相比预测-阈值基线方法获得更高收益。CAM-DF-lite作为其轻量可解释变体，在异质成本和高成本压力下达到最先进水平。在线执行中，CAM-DF使代理暴露于37%更少工具，同时保持任务成功率不变。该方法无需微调底层LLM，可即插即用于现有工具排序流程。
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure. Routers and retrievers can rank candidate tools by relevance, but a ranking alone does not determine how many are worth selecting. Existing approaches leave acquisition under heterogeneous costs unaddressed. We formulate this decision as cost-aware marginal decision-focused stopping (CAM-DF) over ranked tool prefixes, with CAM-DF-lite as a compact interpretable variant. We train directly on the offline gap between stopping now and the best continuation: its sign labels the decision, its magnitude weights each error by the payoff at stake. We prove this objective is Bayes-aligned with the stopping target and that score-only rules are suboptimal under heterogeneous costs. We evaluate on 1,343 tasks across five tool-use domains. On $τ$-bench Retail, CAM-DF attains the highest payoff among deployable methods, with gains over a predict-then-threshold baseline across all five ranking sources and two cost regimes. Our approach is state-of-the-art under heterogeneous costs and high cost pressure, with larger gains under weaker rankings. In live execution, CAM-DF exposes the agent to 37\% fewer tools than full access while maintaining comparable task success. The CAM-DF family is a lightweight pre-execution plugin that turns existing tool rankings into lower-cost acquisition decisions without fine-tuning the underlying LLM.