SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation

The exponential growth of scientific literature has created a pressing need for automated survey generation. Although recent LLM-based agents have shown promise in automating this task, current progress is hindered by the lack of a standardized, scalable evaluation protocol. Existing evaluation methods typically rely on either human evaluation or custom metrics designed to validate specific pipelines, which restricts scalability and hinders fair comparison. To address this, we introduce SurGE, a benchmark and evaluation framework tailored for scientific survey generation. SurGE provides a large-scale retrieval corpus of over one million papers and expert-validated ground-truth surveys. Furthermore, we propose a robust multi-dimensional evaluation protocol that integrates both objective metrics and LLM-based judgments, and empirically verify its high alignment with human experts. Our experiments reveal that while agentic pipelines outperform RAG baselines in fluency and structural quality, they still struggle with citation accuracy, highlighting key directions for future research.