HLER: Human-in-the-Loop Economic Research via Multi-Agent Pipelines for Empirical Discovery
Large language models (LLMs) have enabled agent-based systems that aim to automate scientific research workflows. Most existing approaches focus on fully autonomous discovery, where AI systems generate research ideas, conduct analyses, and produce manuscripts with minimal human involvement. However, empirical research in economics and the social sciences poses additional constraints: research questions must be grounded in available datasets, identification strategies require careful design, and human judgment remains essential for evaluating economic significance. We introduce HLER (Human-in-the-Loop Economic Research), a multi-agent architecture that supports empirical research automatio...
| Authors | Chen Zhu, Xiaolu Wang |
|---|---|
| Primary category | cs.AI |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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