New AI Benchmark RT-QA Uses Executable Code for Real-Time Question Answering
Researchers have unveiled RT-QA, an innovative evaluation framework designed to measure the real-time question answering abilities of AI models, effectively overcoming the shortcomings of traditional static benchmarks. This framework utilizes executable code workflows to gather up-to-date information via web crawling and DOM-based extraction of answers. It features a self-repair system that adjusts to alterations in web page layouts, ensuring long-term reliability. RT-QA spans 12 domains, such as Finance and Sports, comprising 320 Chinese questions divided into three levels of difficulty. The framework conducts thorough evaluations of cutting-edge models, generating real-time ground truth autonomously. This approach aims to reflect the temporal dynamics and ever-changing nature of real-world knowledge, essential for practical search-integrated agents. Detailed findings are available in a preprint on arXiv (arXiv:2604.16349v1), announced as a cross-type abstract.
Key facts
- RT-QA is a dynamic evaluation framework for real-time question answering
- It uses executable code workflows to retrieve up-to-date answers at evaluation time
- The framework includes a self-repair mechanism for adapting to web page structure changes
- It spans 12 domains such as Finance and Sports
- There are 320 Chinese questions categorized into three difficulty levels
- Extensive evaluations of state-of-the-art models are conducted
- The pipeline autonomously generates code for web crawling and DOM-based answer extraction
- The work is detailed in a preprint on arXiv under arXiv:2604.16349v1
Entities
Institutions
- arXiv