arXiv:2605.28836v1 Announce Type: cross Abstract: The Plain Writing Act in the United States requires government documents to be accessible in clear and simple language that the general public can easily understand, yet existing summarization systems struggle to address diverse linguistic and cognitive barriers among general readers. We present NRLB (No Reader Left Behind), a multi-agent framework for plain language summarization that simulates three representative reader groups: elementary school student readers, non-native readers, and readers with attention deficits. NRLB combines template-
The increasing sophistication of large language models makes multi-agent frameworks for nuanced tasks like summarization more feasible, while public policy is pushing for clearer communication.
This development addresses a critical gap in AI's ability to serve diverse user needs, making information more accessible and potentially impacting fields from education to government communication.
AI-powered summarization can move beyond generic outputs to generate context- and user-aware content, catering to specific cognitive and linguistic requirements.
- · AI agents developers
- · Education technology
- · Government agencies
- · Readers with cognitive or linguistic barriers
- · One-size-fits-all content platforms
- · Manual plain language experts (in certain contexts)
Plain language summarization becomes more widely adopted across various sectors.
This improved accessibility could lead to increased public engagement with complex information and政策.
The success of multi-agent simulation could accelerate similar complex, user-centric AI application development.
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Read at arXiv cs.AI