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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.
评测 System One 模型对 Agent 安全输入的分类可靠性与校准
Published Sep 27
Evaluated models Jev 1.13 , Laya, English checkpoint , Decider, approximately 2B and others4 Abstract 离线评测表明，安全判定的总体分数和校准不能代替分组误差和带预算的自动决策检查。Liu 离线评测 System One 模型能否为智能体安全输入给出可自动化的概率决策。问题是分类是否可靠，以及概率能否在指定漏检和误拦上限内支持放行、拦截或复核。
分析稀疏重叠下 LLM judge 与人类一致性的验证误差并规划重叠分配
Published Sep 26
Evaluated models Gemini Flash , Gemini Pro , GPT-4o-mini and others10 Abstract 稀疏重叠主导 judge 验证的错误决策。这篇工作研究标注重叠很稀时，怎样验证 LLM judge 是否足以替代人类，并规划重叠该留多少、怎么分配。 。部署前的标准做法是在校准集上比较 judge 与人类的一致性。
审计 Active Inference Agent 上 LLM 解释器的监督叙述失败
Published Sep 20
Evaluated models GPT-4o , Claude-3-Opus , Gemini Abstract 三组触发下解释流畅但错误，所提缓解都未被评估。作者审计的是挂在智能体上的运行时解释器，而不是智能体本身。操作员读到的是信念与动作的自然语言叙述。作者要问哪些可复现触发会让这段叙述失败，以及失败痕迹是什么样。
刻画单迹监控对跨执行超性质的监督缺口
Published Sep 7
Evaluated models Llama-3.1-8B , Mistral-24B , GPT-4o-mini and others9 Abstract 单迹监督的上界由 TV 决定。论文测量单迹安全监控相对 2-safety 超性质 能看见多少：跨租户泄漏、觉察到被评估、策略性表现不佳都由两次执行之差定义，一条轨迹装不下判定。
提出分片监督，将评判标准分配给独立调用以抵御展示攻击
Published Aug 6
Evaluated models Claude Opus 4.8 , Claude Sonnet 4.6 , Claude Opus 4.6 and others4 Abstract 论文揭示决策负载是大模型监督的独立瓶颈，提出分片与辩论机制有效分离注意力与平衡证据，在多项高难度基准上实现稳健评判。本文系统研究了大语言模型在执行多标准监督与评判时的决策负载瓶颈及其安全风险。
构建 Universal Verifier 核验计算机使用 Agent 轨迹
Published Apr 5
Evaluated models GPT-5.2 , GPT-4o , o4-mini and others8 Abstract 四条设计原则叠成 Universal Verifier，作者称其与人类一致程度相当且假阳性近零。Universal Verifier 给计算机使用轨迹打过程分和结果分，并定位失败。CUAVerifierBench 用人工标签衡量这种验证器。
测量 RAG 系统利用评测秘密抬高自动评测分数的程度
Published Jan 20 You have reached the end