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Oct 12 · Monday

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Found 4 papers
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Model risks

Deception and scheming27Reward hacking10Misalignment and value divergence21Dangerous agent actions7Dangerous capabilities4Inappropriate refusal50Hallucination and fabrication25Bias and discrimination18

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Models and APIs4

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Prompt layer1Model layer3Application layer23Training and data layer3
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  1. Evaluation and benchmarksarXiv 2610.10232

    研究：九种 LLM 说服力评测方法排名仅弱相关

    适配多种已发表说服评测并比较模型排名

    arXiv
    2610.10232
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Claude-Fable-5.1, GPT-5.6-Sol, Kimi-K2.6 and others15
    Abstract说服分数混合能力与意愿，换任务后同一模型的排名可以差很多。

    这项研究比较的是自动化说服评测是否一致，而不是哪一种分数更正确。

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  4. Evaluation and benchmarksarXiv 2606.18936

    SciRisk-Bench：面向 AI4Science 安全的风险维度感知基准

    提出 SciRisk-Bench，按风险维度与学科评测 AI4Science 安全

    arXiv
    2606.18936
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    kimi-k2.6, glm-5.1, gemini-3-flash-preview and others14
    AbstractSciRisk-Bench 以风险维度和学科诊断 AI4Science 安全。

    SciRisk-Bench 是一个按风险维度和科学学科组织的 AI4Science 安全基准，用来看清失败来自哪一种风险机制、出现在哪个学科情境。作者认为 LLM 已介入科研问答、文献分析、实验规划和自主发现，而不安全输出不限于事实错误。现有科学能力基准不测安全，领域安全基准又多集中在化学、医学或生物，或只按宽泛安全类别打分。

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