A domain-enhanced multi-agent framework for trustworthy literature screening in water research.

The integration of artificial intelligence (AI) with sustainable water management holds transformative potential for addressing global sustainability challenges. However, this progress is critically hindered by the slow, labor-intensive construction of large-scale datasets, particularly in identifying the relevant literature from thousands of candidates. Here, we present a hierarchical AI framework that ensures both high accuracy and transparency in literature screening. First, we develop a domain-tailored prompting strategy (3T+RAG) that grounds large language models (LLMs) in structured water-treatment knowledge, thereby improving classification accuracy and screening reliability. Evaluated on three manually curated domain datasets, LLMs achieved reliable literature screening with an average F1-score of 0.88, while operating 79 times faster at only 1.4% of the estimated cost of individual human annotation. To further enhance​ transparency and auditability, we introduce a multi-agent Reviewer-Reviewer-Arbiter (RRA) framework, in which two reviewer agents independently assess literature using the 3T+RAG prompting strategy, and an arbiter agent resolves disputes through reasoning-trace analysis. This architecture distinguishes reviewer disagreements from consistent decisions, allowing human review to focus on disputed articles and targeted quality control. Overall, this study establishes an efficient and reliable pathway for water-treatment literature screening, providing a robust foundation for large-scale dataset construction and accelerating sustainable water management.