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Author

Nizhang Li

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Preprint Sep 2026

SafeDepth: Safety-Aware Token-Level Adaptive Computation

Recent studies suggest that not every token needs to pass through all Transformer layers, motivating token-level adaptive models that selectively skip layers to reduce computation. Our experiments show that these execution choices also affect safety: existing token-level adaptive reasoning models exhibit higher harmful...

Ni-Zhang Li, Ian G. Harris · 0 citations
Preprint Aug 2026

SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills

This work presents SkillSentry, a dynamic safety-testing framework based on adaptive honey worlds, which infers the intended capability boundary of a skill, constructs an LLM-simulated environment with controlled decoy resources, and adaptively generates tasks to explore its behavioral states.

Nizhang Li, Zong-Hao Ying, Xiang-Fan Wu et al. · 0 citations

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