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arXiv · 2604.07655

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs

Yue Huang·Haomin Zhuang·Jiayi Ye·Han Bao·Yanbo Wang·Hang Hua·Siyuan Wu·Pin-Yu Chen·Xiangliang Zhang
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Abstract

Hard-gated safety checkers often over-refuse and misalign with a vendor's model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less useful systems. This work introduces Guardian-as-an-Advisor (GaaA), a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference, keeping the base model operating under its original spec. To support training and evaluation, GuardSet is constructed, a 208k+ multi-domain dataset unifying harmful and harmless cases with targeted robustness and honesty slices. GuardAdvisor is trained via SFT followed by RL to enforce label-explanation consistency. GuardAdvisor attains competitive detection accuracy while enabling the advisory workflow; when used to augment inputs, responses improve over unaugmented prompts. A latency study shows advisor inference uses below 5% of base-model compute and adds only 2-10% end-to-end overhead under realistic harmful-input rates. Overall, GaaA steers models to comply with the model spec, maintaining safety while reducing over-refusal.

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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang. 2026-04-08. Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs. https://arxiv.org/abs/2604.07655

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