The paper introduces a two‑stage framework for auditing false alarms in safety monitors for language‑model agents, treating the problem as a positive‑unlabeled (PU) ranking task. It adapts safe references toward the alarm domain, consolidates ordering from multiple PU models, and refines the ranking with structural guidance, all without requiring alarm safety labels. The method improves macro AUPRC to 0.6444 and recovers 33.3% more false alarms at a 5% review budget compared to leading PU baselines.
By Xichen Yan, Chongyang Gao, Kezhen Chen, Guangyi Zhang, Jiaqi Wu, Lixu Wang
The paper investigates how large language models balance helpfulness and safety by refusing harmful queries while responding to benign ones. It decomposes safety-tuning responses into a boilerplate refusal statement and a rationale, finding that the statement causes false refusals by relying on superficial cues. Training on rationales alone reduces false refusals without compromising safety performance, suggesting that fine‑grained safety supervision is essential for better alignment.
By Minji Kim, Hyounghun Kim
arXiv:2608. 02617v1 Announce Type: cross Abstract: We evaluate whether clinician pairwise preferences provide a reliable signal of clinical safety in large language model (LLM) evaluation using expert feedback from MOOVE (Massive Open Online Validation and Evaluation), a clinician-led platform collecting blinded pairwise preferences alongside multi-criterion rubric ratings.
By Fay Elhassan, David Sasu, Alexandra Kulinkina, Lars Henning Klein, Mary-Anne Hartley
arXiv:2607. 16195v1 Announce Type: new Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF).
By Elena Kopteva, Vitaliy Hlynianyi-Zhuk
arXiv:2602.13576v2 Announce Type: replace-cross
Abstract: Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-langu...
By Ruomeng Ding, Yifei Pang, He Sun, Yizhong Wang, Zhiwei Steven Wu, Zhun Deng
arXiv:2610.17473v1 Announce Type: new
Abstract: Generative safety guards must classify requests correctly and return outputs that applications can parse. Reasoning guards can improve classification a...
By Jingxiang Meng, Aofan Liu, Yaoshuai Ma, Fangxin Liu, Yongbiao Chen
arXiv:2604. 17299v3 Announce Type: replace-cross Abstract: Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones.
By Tiankai Yang, Yi Nian, Xinyuan Li, Ruiyao Xu, Henry Peng Zou, Kaize Ding, Xiyang Hu, Yan Liu, Yue Zhao
The paper introduces DiagSafe, a diagnostic evaluation framework that replaces binary safe/unsafe labels with prompt-level rubrics grounded in AI safety policies. DiagSafe uses a bilingual benchmark with an 84‑label safety taxonomy, comprising DiagBase and DiagAttack subsets, to assess safety and usefulness independently. Experiments show DiagSafe achieves high agreement with human annotations (95.34%) and strong inter‑judge consistency (Cohen’s κ = 0.864), revealing significant differences in unsafe rates and vulnerabilities across leading LLMs, especially in Chinese and multi‑turn contexts.
By Hongyu Li, Xinlong Huang, Zhan Yu, Jing Zhao, Zhe Zhao, Juntao Zhong, Xiong Kuang, Rui Yuan, ZhenXiang Yan, Huilin Xu, Haitao Yu, Lilin Wang, Rui Chen, Blaze Chen, Fengzong Lian, Renfen Hu
arXiv:2609.37837v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly el...
By Haotian Deng, Wenbin Xing, Gang Xu, Tao He, Jinkai Zheng, Chun Li, Zheng Zhu, Ming Li
Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary systems exhibit reliable safety controls, their und...
arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.
By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang
arXiv:2603. 03536v2 Announce Type: replace-cross Abstract: Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction.
By Haochang Hao, Yifan Xu, Xinzhuo Li, Yingqiang Ge, Lu Cheng