SocialLab: An Data-Grounded Framework for Automated Empirical Research in the Social Sciences

Large language models (LLMs) are enabling new approaches to autonomous scientific research. However, empirical social science remains limited by the difficulty of transforming fragmented real-world evidence into reliable research variables and integrating evidence construction with research design, empirical reasoning, and scientific interpretation. To address this challenge, we introduce SocialLab, a data-grounded multi-agent framework for autonomous empirical research in the social sciences. SocialLab coordinates specialized agents across the entire research pipeline, including research planning, evidence construction, empirical analysis, and scientific review. Specifically, the Plan Agent identifies evidence requirements and analysis strategies, the Data Agent constructs and analyzes research datasets from heterogeneous sources, and the Advisor Agent independently reviews research plans, data decisions, and empirical conclusions. A Coordinator orchestrates these agents through a closed-loop workflow, while maintaining alignment with research objectives. We further introduce CSD-Bench, a benchmark for evaluating open-ended empirical research capabilities in the social sciences under heterogeneous, multi-source data settings. Experiments on ReplicatorBench show average gains over Replicator of 18.21, 43.99, 11.14, and 33.14 points in information extraction, research design, execution, and interpretation, respectively. On CSD-Bench, SocialLab achieves a mean quality–coverage harmonic score of 89.75 and a mean research evidence utility score of 88.89. Finally, a case study reveals how individual agents contribute to research design, data validation, and evidence grounding.