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Oct 12 · Monday
Another 240 papers do not have category tags yet and are not included in filtered results.
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出 Safety Operator，用谱优化调节安全指令表达强度
Published Sep 29
Evaluated models Gemma 3 1B , Gemma 3 4B 提出 RLCDAlignBench，零样本检测多种对齐失败
Published Sep 24
Evaluated models Jev (jev-1.13.0) Abstract 一次通用问题已能给多数对齐失败排序，大的误差来自状态和标签。RLCDAlignBench 测量用强化学习做校准决策的 Jev，能否当作对齐失败的零样本检测器。
评估安全监视器并提出双侧梯级微调以减少会答有害请求的漏检
Published Sep 5
Evaluated models Gemma-4-31B-IT , Qwen3-32B , Llama Guard 3 8B and others6 Abstract 论文发现安全监视器大多拦截模型已拒答请求，指出明确度偏差是共性原因，并通过双侧梯级微调提升回答请求的召回表现。
提出 TIER 基准，按威胁隐晦度评测有害提示下的安全行为
Published Sep 4
Evaluated models Qwen1.5-MoE , Gemma-2B , Mistral-7B and others6 提出 CompositeHarm，比较句法操纵与语义危害的跨语言迁移
Published Feb 8
Evaluated models GPT-OSS 20B , LLaMA-3-8B-Instruct , Qwen3-32B 提出 CSSBench 评测轻量模型对中文表面对抗改写的失守与过度拒绝
Published Jan 2
Evaluated models Qwen3-0.6B , Qwen3-1.7B , Qwen3-8B and others10 Abstract CSSBench 用表面扰动和边界查询评轻量模型。CSSBench 评测轻量 LLM 在中文特有表面改写下的安全，并同时测量过度拒绝。要处理的缺口是：英文向护栏和只测干净查询的中文基准，看不清同音、拼音、符号混写、零宽字符这些写法下的失守，也分不清较低风险来自细粒度判断还是粗粒度拒答。作者称轻量模型更需要这种评测。
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