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Oct 13 · Tuesday

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  1. DefensesarXiv 2609.31056

    SCALPEL:用对比稀疏自编码器实现选择性表征层遗忘

    提出 SCALPEL,用对比 SAE 在表征层选择性遗忘特定事实

    Published
    Evaluated models
    Qwen2.5-1.5B-Instruct, Llama-3.2-1B-Instruct, Gemma-2-2B-it
    AbstractSCALPEL 以对比稀疏特征做窄目标表征遗忘,在 TOFU 上与优化基线相当且可检查。

    SCALPEL 是不改基座权重的表征层遗忘方法,面向与保留数据重叠的窄目标。

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  2. DefensesarXiv 2609.01677

    Skill-as-API:面向智能体软件工程的机密多智能体协作协议

    提出 Skill-as-API,隔离多智能体协作中的 skill 正文与系统提示词

    Published
    Abstract协议把 skill 正文留在所有者侧,四层限制发现内容、权限期限和注入拼接。

    Skill-as-API 是面向智能体软件工程的协调协议,用来在跨组织调用时把 skill 的代码和系统提示词留在所有者进程。作者针对三条威胁:经通道或命名空间抽取专有方法,用注入让被调用模型输出系统提示词,以及协作结束后权限继续保留。

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  3. DefensesarXiv 2601.09281

    STaR: A sensitive trajectory regulation method for unlearning in large reasoning models

    提出推理时框架 STaR,抑制大推理模型推理轨迹中的敏感内容

    Published
    Evaluated models
    DeepSeekR1-Distill-Llama-8B
    AbstractSTaR 以推理时轨迹调控做 LRM unlearning,作者称在 R-TOFU 上遗忘与隐私指标更好。

    STaR在推理阶段约束大推理模型的整段 CoT,使指定 forget set 中的敏感内容不出现在中间步骤或最终答案里,且不更新参数。问题来自 LRM 会把敏感知识写进多步推理,而答案级遗忘在 ZeroThink、LessThink 等解码下仍可能泄漏。STaR 先判断查询是否相关并检索遗忘片段,再加安全前缀。

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