Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection
📰 ArXiv cs.AI
Learn how to make failure safe in open-web data collection using a constrained, verifiable agent framework, ensuring reliable web scraper generation from natural-language requirements
Action Steps
- Define a collector taxonomy with six types to categorize web data collection tasks
- Implement template and utility-function constraints to guide LLM output
- Use static analysis to verify collector configurations and ensure error-free execution
- Configure a verifiable agent framework to generate typed JSON collector configurations
- Test and refine the framework using real-world open-web data collection scenarios
Who Needs to Know This
Data scientists and AI engineers working on web data collection projects can benefit from this framework to improve the reliability and efficiency of their data collection pipelines
Key Insight
💡 Constraining LLM output to typed JSON collector configurations can improve the reliability of web scraper generation
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Key Takeaways
Learn how to make failure safe in open-web data collection using a constrained, verifiable agent framework, ensuring reliable web scraper generation from natural-language requirements
Full Article
Title: Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection
Abstract:
arXiv:2607.00035v1 Announce Type: new Abstract: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, broken selectors, schema mismatches, and heterogeneous page structures. We propose a constrained, verifiable agent framework that shifts LLM output from free-form code to typed JSON collector configurations, combining a six-type collector taxonomy, template and utility-function constraints, static Airf
Abstract:
arXiv:2607.00035v1 Announce Type: new Abstract: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, broken selectors, schema mismatches, and heterogeneous page structures. We propose a constrained, verifiable agent framework that shifts LLM output from free-form code to typed JSON collector configurations, combining a six-type collector taxonomy, template and utility-function constraints, static Airf
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