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Oct 12 · Monday

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Found 4 papers
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Defense type

Model hardening2Detection and filtering3Instruction–data separation2

System type

Models and APIs3
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  1. Analysis and theoryarXiv 2610.09004

    研究：去除有害信息不足以证明开源权重模型的抗篡改能力

    论证发布时互信息不能认证开源权重抗微调恢复

    arXiv
    2610.09004
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    EleutherAI/deep-ignorance-e2e-strong-filter, EleutherAI/deep-ignorance-unfiltered, Google BERT-Tiny and others6
    Abstract作者称零信息仍可一步恢复，表观抗微调可来自参数化。

    作者研究开源权重模型的篡改抵抗：发布时的互信息是否足以保证微调恢复很慢。

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  2. Analysis and theoryarXiv 2610.04375

    Paper reveals that LLM agent tool calls are rewritten during execution paths, and proposes IntAct as a fix

    提出 IEC 与 IntAct，测量并修复 Agent 工具调用的路径篡改

    arXiv
    2610.04375
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    qwen3-coder-plus, Qwen2.5-Coder-32B, Qwen2.5-72B and others4

    Abstract论文界定工具调用在执行路径中的意图偏离问题，提出无执行归因协议与跳级修复，为智能体工程提供评测与设计准则。

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  3. Analysis and theoryarXiv 2609.35677

    RLVR 中的验证器错误：奖励作弊、反馈局限与选择性控制

    刻画不完美验证器下 RLVR 奖励黑客的增长并用投影审计校正实现选择性控制

    arXiv
    2609.35677
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    log linear policy, neural policy, Qwen2-0.5B
    Abstract固定验证器的 RLVR 会把验收与黑客绑在一起。

    作者分析固定、不完美验证器下的 RLVR：验证器无假阴性，因而同时奖励正确回答和被接受错误，平均奖励只看总验收概率。

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  4. Analysis and theoryarXiv 2506.06404

    研究揭示价值对齐 LLM 的意外危害并提出上下文对齐方法

    分析按 Schwartz 价值对齐后的意外危害，并提出上下文对齐方法

    arXiv
    2506.06404
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-2 7B, Llama-3.1 8B, Gemma-2 9B and others6
    Abstract价值对齐会随 Schwartz 价值改变有害倾向。

    作者研究按 Schwartz 价值对齐的 LLM 为何在安全评测里更易有害，并用提示做了一个不直说要安全的缓解试验。

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