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Oct 12 · Monday

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  1. 研究发现大语言模型被要求不诚实作答时推理 token 数上升

    检验推理 token 数能否区分被提示的诚实与不诚实作答

    Published
    Evaluated models
    Gemma-4-e4b, Qwen-3.5-9b, GPT-OSS-120B
    Abstract被提示不诚实时推理 token 组水平更高。

    三个推理模型在被提示的回答政策下出现组水平推理 token 差异。作者把它当作不读思维链内容的 black-box 候选信号,而不是自发欺骗检测器。

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  2. 研究:隐蔽推理必然留下信息痕迹,但思维链未必可读

    分析隐蔽推理在思维链监控下的信息足迹下界与不可读性

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Qwen 3.5 397B-A17B and others12

    Abstract论文从理论与实证探明了思维链监控的边界:复杂隐蔽计算必然泄露信息足迹且需承担长度税,但该足迹可被在线加密而不可读。

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  3. 研究:评测感知分能力与安全两种框架,能力框架更易预测合规

    把口头评测感知分成能力与安全框架并检验对服从的预测

    Published
    Evaluated models
    Qwen3-32B, OLMo-3-32B-Think
    Abstract能力型与安全型口头 eval-awareness 对服从的符号相反,聚合抑制率因此不能代表安全相关成分。

    作者分析 Qwen3-32B 在安全评测中的口头 eval-awareness,认为它不是单一、行为上均匀的量。

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