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Oct 12 · Monday

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Found 5 papers
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Oversight failure3

System type

Models and APIs5AI Agent51coding agent14Browser and computer use3Multi-agent systems5

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Model layer4

System component

Memory1Guardrails and judges30Monitors4Reasoning chains6Fine-tuning and open weights3Vision19Audio8Video7Image generation7Multimodal35
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  1. Evaluation and benchmarksarXiv 2610.04737

    PyINE：用可验证 Python 执行轨迹研究推理模型的捷径与监督

    提出 PyINE 基准，训练捷径跟随模型并比较监督者

    arXiv
    2610.04737
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    shortcut-following model organism, gpt-4o, gpt-5 and others12
    AbstractPyINE 用可验证代码执行研究捷径失败的监督覆盖与成本权衡。

    PyINE 是用可执行程序研究推理模型引出与监督的框架，PyINE-v1 是其第一次发布。

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  2. Evaluation and benchmarksarXiv 2610.02741

    研究系统评估循环语言模型的思维链可监控性

    系统评估循环语言模型加深 loop 后的 CoT 可监控性

    arXiv
    2610.02741
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Ouro-1.4B-Thinking, Ouro-2.6B-Thinking, Nanbeige4.2-3B and others4
    Abstract加深 loop 可在部分压力测试任务降低可监测性，循环架构本身并不必然更难监测。

    这项工作评估 LoopLM 的 CoT 可监测性：加深共享层的重复次数，以及“有循环结构”本身，会不会让决策关键因素更少出现在可监测文本里。

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  3. Evaluation and benchmarksarXiv 2609.39473

    FAME：用反事实推理评测自主智能体的虚假记忆

    提出 FAME，用反事实概念漂移评测智能体虚假记忆

    arXiv
    2609.39473
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3B
    AbstractFAME 用反事实引起的隐状态概念漂移，在出答案前区分忠实记忆与虚假记忆。

    FAME 是一个免训练的评测框架，用来判断自主智能体的记忆在虚假相关、环境偏移或知识冲突下是否已变成虚假记忆。。

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  4. Evaluation and benchmarksarXiv 2609.05797

    Paper finds safety monitors mainly block requests the model would already refuse

    评估安全监视器并提出双侧梯级微调以减少会答有害请求的漏检

    arXiv
    2609.05797
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Gemma-4-31B-IT, Qwen3-32B, Llama Guard 3 8B and others6

    Abstract论文发现安全监视器大多拦截模型已拒答请求，指出明确度偏差是共性原因，并通过双侧梯级微调提升回答请求的召回表现。

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  5. Evaluation and benchmarksarXiv 2602.08877

    Automated red-teaming pipeline stress-tests alignment audits, finds prompt-level strategic deception can render audits ineffective

    提出自动化红队流水线，用系统提示词压测对齐审计能否抵御策略性欺骗

    arXiv
    2602.08877
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Gemma 2 9B Instruct, Llama 3.3 70B Instruct

    Abstract通过红队提示词揭示现有审计方法易受策略欺骗，并在激活层面诱导高置信度错误推断。

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