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Oct 13 · Tuesday
Another 215 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.
提出基于决策前隐藏状态的检测与引导，预测并抑制智能体欺骗
Published Oct 5
Evaluated models Qwen3-14B Abstract 论文展示了智能体自发欺骗可在行为显现前由内部表征预测，并通过激活引导证实了表征的行为可操作性。
提出有守卫最小步激活引导，只在关机语境把继续偏好转向接受关机
Published Sep 24
Evaluated models Qwen3.5-0.8B 用词级归因指导同义词替换，翻转提示注入检测并验证越狱
Published Sep 22
Evaluated models Prompt Guard 2 (86M) , Llama 3.1 8B 提出 MonoTM，用 SAE 单义特征估计文档主题混合并生成主题描述
Published Sep 9 Abstract MonoTM 分开估计混合与解释主题，三套基准上二者偏好的 SAE 配置不同。MonoTM 是建在文档嵌入 SAE 上的可解释主题模型：混合估计用全部 SAE 特征，主题描述只用另一套配置里通过验证的语义特征。
用 SAE 追踪稠密检索查询扩展的潜变量并用激活转向验证
Published Sep 7
Evaluated models Qwen3-Embedding-0.6B（Qwen-Embed） , SimLM Abstract SAE 追踪稠密检索 QE 的潜变量，转向在四基准上比文本扩展更稳。作者用稀疏自编码器分析稠密检索里的查询扩展：现成检索器不微调时，拼接扩展文本往往增益有限甚至变差，而最终指标和嵌入整体偏移不足以说明内部发生了什么。
提出 influence score，量化提示注入检测器各注意力头的有效贡献
Published Sep 4
Evaluated models DeBERTa-v3-base prompt-injection classifier Abstract 该分数把注意力头的方向与残差贡献连到分类，并分开正确与错误预测。这项工作在推理时给提示注入检测分类器的注意力头一个影响分数，用来看分类决策从哪些头来。缺口是电路分析往往要重插桩，只看输出又不知道内部组件的贡献。作者希望同时看到决策在哪一层形成、哪些头在推，以及正确与错误是否对应不同机制。
在写入点定位拒答方向，经刚性旋转跨架构迁移并门控放大
Published Sep 4
Evaluated models Falcon-Mamba-7B , Falcon3-Mamba-7B , Llama-3.1-8B and others8 Abstract 拒答方向可经刚性旋转跨架构迁移，但必须在各架构的写入点读取。作者研究拒答表示是否随架构改变，结论是表示共享、读取位置随架构变。
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