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Oct 12 · Monday

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Found 4 papers
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Models and APIs4

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Prompt layer4Model layer4Application layer26

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Memory1Guardrails and judges4Monitors5Reasoning chains5Vision8Audio4Video5Image generation3Multimodal16
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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 2609.06991

    AV-SafetyBench：面向文本到音视频生成的安全基准

    提出 AV-SafetyBench 评测文生音视频模型的不安全生成

    arXiv
    2609.06991
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    LTX-2.3, Ovi-1.1, JavisDiT++ and others7
    AbstractAV-SafetyBench 用三视角评五个 T2AV 模型，显示只看视频会漏掉音频与联合风险。

    AV-SafetyBench 是面向文本生成音视频（T2AV）的安全基准：一条提示词同时规定画面和音频，评测同时看两轨以及它们的交互。作者要测的是，不安全含义不只出现在画面上：它可以主要由语音或声音承载，也可以只有两轨合起来才成立。T2VSafetyBench 不评音频，TTA-Bench 不评与画面的交互，作者认为二者都盖不住这些路径。

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