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Oct 12 · Monday

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  1. 研究:智能体欺骗行为在外部显现前已可从内部表征预测

    提出基于决策前隐藏状态的检测与引导,预测并抑制智能体欺骗

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3-14B

    Abstract论文展示了智能体自发欺骗可在行为显现前由内部表征预测,并通过激活引导证实了表征的行为可操作性。

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  2. Study reveals that self-reported and behavioral LLM risk preferences are controlled by nearly orthogonal representation directions

    揭示风险自述与实际行为由近正交表征分别控制

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, OLMo-3-7B-Instruct and others4
    Abstract论文证明大模型自述与行为脱节根植于表征近正交性,自述无法作为行为审计依据。

    这篇论文从内部表征与因果干预视角,系统研究了大语言模型在风险承担(Risk-Taking)场景下自我报告与实际决策脱节的根本机制。

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  3. Study: LLM agents retain their original premises internally even when conforming to the majority

    用 J-lens 检测辩论中屈从多数的智能体是否仍内部表征原前提

    Published
    Evaluated models
    Qwen3.5-4B, Qwen3.6-27B, Gemma-4-E4B-it and others4

    Abstract揭示 LLM 智能体屈从多数时内部仍表征原推理前提,指出辩论表态共识可能夸大真实认同。

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  4. ActFlow:无需微调为故意藏拙模型制造激活向量以进行引导

    提出 ActFlow,用少量正确标签为藏拙模型制造激活引导

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.2
    AbstractActFlow 用 k 个正确标签制造共享转向偏移,在六锁上接近微调且方向近乎正交于诚实方向。

    Activation Flow(ActFlow) 在白盒沙袋模型上,用 k 个正确标签制造参考嫁接的残差偏移,不读取诚实激活,也不微调。

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  5. 因果模型定位并解锁模型中的故意藏拙行为

    提出因果模型定位残差流中的藏拙轴,并用单层 graft 在推理时解锁

    Published
    Evaluated models
    Qwen2.5-7B, Llama-3-8B, Mistral-7B
    Abstract因果模型规定单层 graft 的窗口。

    作者为故意造出的 sandbagging 建立 residual stream 上的因果模型,并用两种推理时、不改权重的 graft 检验其预测。

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  6. 研究:六个开源权重模型的评测感知可线性解码,但与口头表达弱相关

    用探针与激活导向分析语言模型的评测意识表征及言语化

    Published
    Evaluated models
    Qwen3-8B, Qwen3-32B, Olmo3-7B and others14

    Abstract论文系统证明大模型普遍具备评估与部署的隐层表征,但与言语化弱耦合,激活干预可因果诱导言语化且 SFT 阶段起关键稳定作用。

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