# An AI-Resilient Text Rendering Technique for Reading and Skimming Documents

arXiv: https://arxiv.org/abs/2401.10873 · HTML: https://mlsanity.com/paper/2401.10873/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2024.

## Built on by (most cited first, 11 of 11)

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- [Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agents](https://mlsanity.com/paper/2507.05820/) (2025)
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- [DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decomposition](https://mlsanity.com/paper/2507.18802/) (2025)
- [Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making](https://mlsanity.com/paper/2504.03207/) (2025)
- [HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding](https://mlsanity.com/paper/2505.22906/) (2025)
- [TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models](https://mlsanity.com/paper/2507.18945/) (2025)
- [AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface](https://mlsanity.com/paper/2610.18724/) (2026)

## Abstract (arXiv)

Readers find text difficult to consume for many reasons. Summarization can address some of these difficulties, but introduce others, such as omitting, misrepresenting, or hallucinating information, which can be hard for a reader to notice. One approach to addressing this problem is to instead modify how the original text is rendered to make important information more salient. We introduce Grammar-Preserving Text Saliency Modulation (GP-TSM), a text rendering method with a novel means of identifying what to de-emphasize. Specifically, GP-TSM uses a recursive sentence compression method to identify successive levels of detail beyond the core meaning of a passage, which are de-emphasized by rendering words in successively lighter but still legible gray text. In a lab study (n=18), participants preferred GP-TSM over pre-existing word-level text rendering methods and were able to answer GRE reading comprehension questions more efficiently.