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arXiv · 2607.24769

LLM Scheming Inversely Scales with Pretraining Language Coverage

Nathan Truong·Aryan Panda·Rayming Ye·Zoe Sun·Maheep Chaudhary
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Abstract

With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has empirically demonstrated in-context scheming -- the covert pursuit of misaligned objectives while feigning alignment -- in frontier language models, most work has been performed exclusively in English, leaving a major gap in multilingual safety. We apply Petri, an open-source automated auditing framework, to Qwen3-30B-A3B to evaluate deceptive and scheming behaviors across multiple languages. Our findings suggest that scheming scores are inversely correlated with the estimated pretraining language coverage, with low-resource languages averaging 34.2\% higher scores compared to high-resource languages on a five-category scheming index. Furthermore, we find that the effect of estimated pretraining language coverage is not uniform across scheming behaviors.

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Nathan Truong, Aryan Panda, Rayming Ye, Zoe Sun, Maheep Chaudhary. 2026-06-09. LLM Scheming Inversely Scales with Pretraining Language Coverage. https://arxiv.org/abs/2607.24769

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