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

Researchers introduced NRLB, a multi-agent AI framework designed to create plain language summaries accessible to diverse reader groups including elementary students, non-native speakers, and those with attention deficits. The system combines template-based planning with iterative refinement to improve readability while maintaining factual accuracy, achieving human preference rates of 55-76% in evaluations.

NRLB addresses a critical gap between government compliance requirements and practical accessibility in AI-generated content. The Plain Writing Act mandates clear communication in official documents, yet most summarization systems fail to account for varying literacy levels and cognitive needs. This research demonstrates that accessibility isn't a secondary feature but requires deliberate architectural choices—simulating reader personas and iteratively refining outputs based on their specific barriers.

The framework's approach reflects broader recognition in AI development that one-size-fits-all solutions underserve vulnerable populations. By systematizing the detection of difficult terminology, missing context, and confusing sentence structures, NRLB moves beyond generic readability metrics like Flesch-Kincaid scores. This methodology has applications beyond government documents, extending to healthcare communications, financial disclosures, and educational materials where clarity directly impacts outcomes.

For the AI industry, NRLB validates that multi-agent architectures and reader-centric design patterns enhance both accessibility and user satisfaction. The 55-76% human preference rates suggest significant market potential for accessibility-focused AI tools, particularly as regulatory pressure increases for inclusive digital communication. Organizations handling sensitive information—insurance companies, medical providers, financial institutions—face growing demands to serve diverse literacy populations effectively.

Looking forward, the framework's success could influence how AI companies design summarization and content-simplification tools. Integration of similar multi-persona testing into standard AI evaluation benchmarks may become expected practice. The research also suggests that accessibility-first development yields better overall product quality, potentially reshaping how companies prioritize feature development.

• →NRLB's multi-agent framework simulates three reader personas to systematically improve accessibility and readability of summarized content.
• →The system achieved 55-76% human preference rates while maintaining factual accuracy, validating accessibility-focused design in AI systems.
• →Template-based planning combined with iterative refinement successfully identifies and resolves linguistic and cognitive barriers in text.
• →The framework addresses regulatory compliance needs while demonstrating commercial potential for accessibility-first AI tools.
• →Success with reader personas suggests multi-perspective testing should become standard practice in AI content generation evaluation.