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Oct 12 · Monday
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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.
Proposes a local dual-model closed loop to generate edge scenarios for autonomous system safety assurance
Published Oct 9
Evaluated models PPO Abstract Local closed-loop generation of JSON edge cases.The authors built a local agentic loop that runs on a laptop to generate edge cases for safety arguments of autonomous systems, rather than releasing a new public test set.
Proposes EndoRSI, recursively improving endoscopic robot policies with physical evidence
Published Oct 9
Evaluated models Qwen3-VL-4B A survey of risks, defenses, and evaluation in the six-stage lifecycle of world models
Published Oct 9 Abstract Safety is written as a property of the prediction-decision closed loop; claims must be linked to observed consequences and versions.World model safety is organized by the nature of the prediction-decision loop, not by isolated prediction scores.
Propose COAP, which uses stateful code to control a robot at test time without a model
Published Oct 9 提出 Inspect Robots，在真实机器人上评测具身模型的能力与拒答
Published Oct 5
Evaluated models Astra（GPT-6 Astra） , GPT-6 Sol , GPT-5.6 Sol and others7 提出 CrashSim 用真实事故先验引导扩散仿真并构建 nuCrash 评测自动驾驶规划器
Published Oct 3
Evaluated models Lane-graph planner , PDM-closed planner , IL planner and others5 Abstract CrashSim 迁入真实事故先验生成场景。CrashSim 用检索到的真实事故先验引导扩散式多车仿真，为自动驾驶安全评测生成碰撞与近碰撞场景。 。
提出 SceneFactory-3D，用路况反事实评测驾驶策略的闭环安全性
Published Oct 2
Evaluated models Frozen L0 , L0-T , L1 and others5 Abstract SceneFactory-3D 固定场景只改路况。SceneFactory-3D 是 GPU 批处理的多智能体驾驶仿真器，用来在场景和控制器固定时只改路面物理，并比较闭环结果。
审计机器人安全监控的细化缺口，比较表面计划与图细化轨迹的裁决
Published Oct 2 Abstract 表面轨迹可漏掉图诱导的禁区进入。作者审计 RoboGuard 的一个假设：对高层动作轨迹的安全裁决，在补上语义图诱导的导航和已记录隐式效应之后是否仍然成立。做法是不改验证器，并固定同一条 LTL。
提出 DrivingBench，评测通用视觉语言模型能否闭环驾驶真车
Published Sep 30
Evaluated models GPT-6 Astra , Claude Fable 5.1 , Grok 4.6 and others4 Abstract 仅 Astra 第二次跑完低速锥桶路线。DrivingBench 评测通用 VLM 能否自己闭环开真车，推理延迟也算进任务。
提出 NAVSAFE-∞ 写真闭环基准，评测端到端驾驶策略的闭环安全
Published Sep 22
Evaluated models LTF , DiffusionDrive , DrivoR and others13 Abstract 写真事件级闭环基准显示开环高分不能稳定变成闭环安全。NAVSAFE-∞ 用写真、事件级闭环仿真检查端到端驾驶策略的安全，而不是只看开环轨迹分数。
提出 DriveMCP 框架，将感知、交规检索与车辆遥测接成可审计驾驶辅助层
Published Sep 15 Abstract DriveMCP 用可审计专家工具和 RSS/TTC 仲裁做驾驶辅助，作者称违章与超速低于三个 VLM 基线。DriveMCP 是面向高级驾驶辅助的模块化智能体框架，把感知、合规、车辆状态和安全仲裁拆开并留下审计轨迹。
提出评估合同，检验合规监测数据能否支持比较性安全主张
Published Sep 12 Abstract 比较主张要先对齐能力、结果、暴露、领域、采集和对照。作者给出的是评测效度立场：部署后的运行数据要支持比较主张，必须先写明测量与主张如何对齐。
提出 S³，用 guard agent 编排阶段安全技能防御智能体多阶段风险
Published Aug 3
Evaluated models DeepSeek-V4-Pro , DeepSeek-V4-Flash 提出 LABSHIELD，评测科学实验室智能体的安全推理与规划
Published Mar 12
Evaluated models GPT-5.2 , GPT-o3 , GPT-o4-mini and others20 提出 Blindfold，用动作级操纵越狱具身 LLM
Published Mar 2
Evaluated models Llama-3.1-8B , DeepSeek-R1-14B , Gemma-3-27B and others8 Abstract Blindfold 以动作级代理规划越狱具身 LLM，作者称可绕过语义检查并在仿真与真机生效。Blindfold 是面向具身 LLM 规划模块的 no-box 攻击，把恶意意图改写成看似良性的动作序列。
提出 PINA，迭代优化针对导航智能体的提示注入
Published Jan 20
Evaluated models GPT-3.5-turbo , GPT-4 , Balcı et al. [2] Abstract PINA 在黑盒下优化导航提示注入。PINA 是面向基于 LLM 的导航智能体、在黑盒条件下优化提示注入的框架。
Attacks arXiv 2505.23266 · May 29, 2025 提出 AdvOF，生成对抗物体误导视觉语言导航智能体的场景感知
Published May 29, 2025
Evaluated models Lseg , Clip , ViT-L/16 and others5 Abstract AdvOF 以多视角融合生成对抗物体。AdvOF 是针对 VLM 驱动 VLN 服务导航的对抗物体攻击。
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