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Oct 12 · Monday
All papers Found 10 papers Another 265 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.
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.
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。
提出 FAIL BANK，用运行时 CBF 反馈自进化更新 VLA 策略
Published Sep 30
Evaluated models π0.5 , π0 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 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 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 VILTA，在闭环训练中用 VLM 编辑邻车轨迹以提升驾驶策略鲁棒性
Published Jan 19 Abstract VILTA 在闭环中编辑邻车轨迹以训练驾驶策略。VILTA 是一个把视觉语言模型放进自动驾驶闭环训练的框架，用来应对长尾危险场景不足。
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