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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 the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints
Published Oct 9 Abstract FAITH separates safety filtering from reward optimization.FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.
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 评测文本守卫与激活探针能否拦住机器人隐含伤害指令
Published Oct 5
Evaluated models π0.5 , OpenVLA-OFT , Llama Guard 3 and others10 提出 VICS-G 可行未来解码，重排冻结 VLA 策略的动作候选
Published Oct 4
Evaluated models SafeVLA (task-optimized / base) , SafeVLA (safety-aligned / safe) Abstract 作者用可行未来解码重排冻结 VLA 的下一步，并称 VICS-G 降低观测代价且完成率接近策略采样。训练无关解码器 VICS 在冻结 VLA 的推理阶段重排动作块，使当前选择带上该动作仍可能留下的安全完成质量。
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。
测量并预测具身 VLM 规划器的任务拒答可变性
Published Sep 30
Evaluated models gemini-robotics-er-2-preview , gemini-robotics-er-1.6-preview Abstract 日常物体能翻转一部分已给出的拒答，可翻转程度取决于任务且可被本地代理预测。作者测量 constitution 守护的具身 VLM 规划器：一次拒答在只往场景里放一个日常物体之后还保不保持得住，并把这种易翻转程度称为任务的 malleability 。
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 SafeStage 基准，分阶段评测视觉语言机器人操作的物理安全
Published Sep 18
Evaluated models π0.5 , GR00T N1.7 , DreamZero and others4 提出 TrojanWorld，用物理触发器对世界模型智能体植入想象引导后门
Published Sep 7
Evaluated models TD-MPC2 , DreamerV3 , R2-Dreamer Abstract TrojanWorld 揭示了世界模型智能体的供应链后门风险，通过在世界模型中操纵想象可实现对闭环动作的持续控制。
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 HazardArena 基准，用孪生场景评测 VLA 的语义安全
Published Apr 14
Evaluated models OpenVLA-OFT , π0 , NORA and others4 Abstract safe-only 微调提升良性执行，也提升匹配不安全孪生上的危险完成。HazardArena 评测 VLA 在运动要求不变时，能否因语义风险而停手。
提出结构化 JSON 后门，经投毒 LoRA 在机器人控制中触发恶意动作
Published Apr 5
Evaluated models Llama-3.1-8B-Instruct , Gemma-2-9B-IT , DeepSeek-R1-Distill-Llama-8B and others4 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。
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