cs.AI提交于 2026-10-15已译

小型语言模型应主导吗？云端编写代理程序中本地模型作为解释器的研究

Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs

Boris Radovi\v{c} · Marco Canini · Veljko Pejovi\'c

中文摘要

执行用户任务最准确的智能体由大型云端模型驱动，需读取用户私有数据。边缘设备运行的小模型虽保持数据本地化，但效果较差。第三种方案是由云端模型编写程序供设备执行，数据虽本地化，但程序在未见数据前编写，无法处理运行时才出现的内容解释。本文探索一种中间架构，云端编写的程序通过普通函数调用将有限问题委托给本地小模型。在 AppWorld 基准上，该架构在所有六种骨干模型与测试分区组合中，均提升了纯代码智能体的成功率，最高提升 14.9 个百分点，在保持用户数据在设备上的同时，大幅缩小了与云端 ReAct 循环的差距。

关键要点

01云端模型编写程序无法处理运行时动态数据，导致任务失败。
02提出中间架构，通过函数调用将数据解释任务委托给本地小模型。
03在 AppWorld 上，该架构使纯代码智能体成功率最高提升 14.9 个百分点。
04该方法在保持数据本地化的同时，缩小了与云端 ReAct 智能体的性能差距。

解读

尚无解读。

原始英文摘要

arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the user. Agents powered by models small enough to run on edge devices keep that data local, but yield far worse results. A third option is to have cloud models write a program that devices execute: the data stays local, but as the program is written before any data is seen, it fails whenever a step depends on interpreting content that appears only at run time. We explore an intermediate architecture, in which the cloud-written program defers bounded questions over user data to a small local model through an ordinary function call. On AppWorld this improves the success rate of a code-only agent in all six combinations of backbone model and test partition we evaluate, by up to 14.9pp, recovering a substantial part of the gap to a cloud-hosted ReAct loop while keeping user data on the device.