EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Data

2026-09-16

AI Summary

研究通过EconSkills框架解决了Web代理在重复访问相同网站时技能重用的问题，采用参数化标准操作程序来提高经济数据检索效率。

Abstract

Web agents often revisit the same sites, yet most evaluations discard the procedures learned in earlier successful interactions. We introduce EconSkills, a skill library and evaluation framework that distills verified EconWebArena trajectories into parameterized standard operating procedures for retrieving live economic data. Each skill records its scope, navigation procedure, site-specific guidance, verification checks, and recovery steps while replacing source-instance values with placeholders. EconSkills separates two questions: whether a known relevant procedure transfers to a held-out task, and whether an agent can retain that benefit when selecting from a library. In controlled transfer, matched skills improve success over no-skill prompting and require fewer steps on paired successes, while abstraction is substantially more effective than replaying raw trajectories. At library scale, retrieval is competitive with the no-skill baseline overall and performs best on directly covered tasks; coverage-stratified outcomes show that approximate matches on uncovered tasks offset these gains. Browser trajectories further identify when procedural guidance shortens portal-specific navigation and when semantic verification remains necessary. These results establish that reusable economic web procedures can transfer across task instances and provide a concrete design target for coverage-aware selection and context delivery.

Problem

• Web Agents
• Skill Transfer
• Economic Data
• Standard Operating Procedures
• Verification Checks

Innovation

• EconSkills
• Skill Transfer
• Economic Data Retrieval
• Controlled Transfer
• Abstraction