ResearcharXiv cs.AI

Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

#data collection#ai framework#web scraping#llm#verification

English

The article presents a constrained, verifiable agent framework for open-web data collection, addressing issues of reliability in generating web scrapers from natural language. By utilizing a typed JSON configuration and various constraints, the framework demonstrates improved execution stability and efficiency, achieving zero execution-stage LLM tokens in verified tasks.

中文

本文提出了一种用于开放网络数据收集的受限可验证代理框架，解决了从自然语言生成网络爬虫时的可靠性问题。通过利用类型化的JSON配置和各种约束，该框架在验证任务中实现了零执行阶段LLM令牌，提高了执行稳定性和效率。