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Oct 13 · Tuesday
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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 agent plasticity 指标衡量智能体通过经验自我改进的效率
Published Oct 7
Evaluated models Claude Fable 5 , Claude Opus 5 , GPT-5.6 Sol and others10 Abstract 作者用固定权重协议比较前沿模型把经验写成制品后的留出增益与习得效率。作者用 agent plasticity 衡量一件事:权重冻结时,智能体把经验写成可继承制品,再换成留出能力增益,这个转换有多省。
评测编码智能体 harness 安全机制在对抗任务中的防护效果
Published Oct 6 评测容器、权限规则与沙箱能否区分编译器与编码 Agent 的文件读取
Published Sep 29 Abstract 此 harness 表达不了编译器可读而智能体不可读,五条路径两边都不覆盖。作者在物理流求解器上检查一套编码智能体配置:编译器必须读本组材料与反应模型,这些字节又不得进入模型上下文或离开本机的记录。
提出 MMSkillRisk 基准,评测多模态技能图像中的隐藏指令诱导 Agent 越权操作
Published Sep 28
Evaluated models GPT-5.6-sol , Kimi-K2.6 , Kimi-K3 and others6 Abstract 论文提出 MMSkillRisk 基准与 NCVA 攻击,揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。
提出 SkillMisevo-Bench,量化自改进 Agent 的技能误演化
Published Aug 13
Evaluated models Claude Code , Codex , Hermes and others4 构建 ContextWeave,评测办公工作流智能体记忆的下游增益
Published Aug 5
Evaluated models DeepSeek-V4-Pro , GPT-5.5 , GLM-5.1 and others5 Abstract ContextWeave 用真实办公任务流检验记忆是否改善工作区质量与偏好对齐。ContextWeave 是面向语言智能体记忆的纵向基准,用来看召回先前经验能否让后续办公任务做得更好。
评测编码 Agent 记忆文件中提示注入的跨会话影响
Published Jul 16
Evaluated models Claude Haiku 4.5 , Claude Opus 4.7 , GPT-5.2 and others4 Abstract 已植入记忆文件的载荷可以跨会话影响 Claude Code 与 Codex,攻击成功与载荷是否留下是两种不同的失败。Bad Memory 在沙箱合成工作区里评测文件型持久记忆上的提示注入,比较载荷能否改变当前会话,以及能否在后续会话中留下或叠加。
提出 AuthBench 评测编码 Agent 最小权限,并验证充足性-紧缩性分解
Published May 14
Evaluated models GPT-5 , GPT-5.3-Codex , GPT-5.4 and others9 Abstract 论文提出权限边界推断基准与两阶段分解方法,有效降低安全暴露。本文研究代码智能体在执行终端任务前自主推断最小文件权限边界的能力。
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