Author

Yu-Zhu Mao

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#natural language process... Preprint Oct 2026

Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability

We connect the spurious-reward paradox to a model's reachability and propose random-reward reinforcement learning (RL) as a useful tool for the probing enterprise, addressing a decade-long debate over what probing performance actually reveals about a model. There are two prevailing explanations for the surprising findi...

Yu-Zhu Mao, Lei Yu, Zi-Ning Zhu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromising task utility remains a major challenge. Existing approaches preserve most of the original semantic content to maintain downstream performance, but this also leaves exp...

Yu-Zhu Mao, Liang Zhao · 0 citations