No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand

A new multi-agent framework called NRLB aims to make automated summaries readable for elementary students, non-native speakers, and readers with attention deficits, addressing a gap left by current systems that fail to accommodate diverse cognitive and linguistic needs [1][2]. The framework, formally named "No Reader Left Behind," was submitted to arXiv on 12 Apr 2026 [1]. It is designed to help institutions comply with the U.S. Plain Writing Act, which mandates that government documents be presented in clear, simple language the general public can understand [2]. Researchers note that existing summarization tools often fall short because they do not account for the varied ways people process text [1]. NRLB operates by simulating three distinct reader profiles: elementary school students, non-native English readers, and individuals with attention deficits [2]. The system uses a template-based planning approach combined with iterative, reader-oriented refinement. This process allows it to systematically detect and resolve difficult terms, fill in missing contexts, and untangle confusing sentences before producing a final summary [1][2]. In evaluations across multiple datasets, the framework delivered consistent gains in readability without sacrificing factual accuracy [2]. Human annotators confirmed the improvement: preference rates for NRLB-generated summaries ranged from 55% to 76% when compared against baseline outputs [1][2]. The researchers argue these results show the model can produce plain-language summaries that remain faithful to their source material while reaching a broader audience [2]. The work arrives as pressure mounts on public agencies to improve document accessibility. The Plain Writing Act, signed into law in 2010, requires federal agencies to use clear communication that the public can understand and use [2]. Automated tools that can adapt text for multiple reader profiles could reduce the manual effort currently required to meet those standards. The NRLB paper was released through arXivLabs, a framework that lets collaborators develop and share new features on the arXiv platform [1].