Text simplification aims to make complex text more accessible by reducing linguistic complexity while preserving the original meaning. However, progress in this area remains limited for mid-resource and low-resource languages due to the scarcity of high-quality data. To address this gap, we introduce OasisSimp, a multilingual dataset for sentence-level text simplification covering five languages: English, Sinhala, Tamil, Pashto, and Thai. Among these, no prior sentence simplification datasets exist for Thai, Pashto, and Tamil, while limited data is available for Sinhala. Each language simplification dataset was created through direct human annotation, where trained annotators followed detailed guidelines to simplify sentences while maintaining meaning, fluency, and grammatical correctness. We evaluate eight open-weight multilingual Large Language Models (LLMs) on OasisSimp and observe substantial performance disparities between high-resource and low-resource languages, highlighting the simplification challenges in multilingual settings. OasisSimp thus provides both a valuable multilingual resource and a challenging benchmark, revealing the limitations of current LLM-based simplification methods and paving the way for future research in low-resource text simplification. The dataset will be open-sourced upon acceptance.

Generative Engine Optimization (GEO) lets content owners rewrite web content to increase their visibility in generative systems. In recommendation agents, this creates a risk that sources controlled by sellers make flawed products appear better supported than they are. We study this risk at the generation stage by asking whether recommendation agents continue to make decisions that align with user utility when these sources are rewritten for GEO. To make this question measurable, we construct SafeGEO, an evaluation suite with 22 GEO attack variants across 600 recommendation cases. We empirically show that GEO attacks can promote flawed target products: they increase the rate at which such flawed products enter the recommendation set by up to 83.2 percentage points (pp). We further study whether agent-side design choices can mitigate this risk and show that simple defenses reduce harmful target promotion by up to 39.2 pp. These gains are substantial but do not restore the no-GEO performance, showing that GEO remains a serious risk despite mitigation.