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Oct 12 · Monday

All papers

Found 5 papers
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Model risks

Deception and scheming12Reward hacking4Misalignment and value divergence8Dangerous agent actions5

System type

AI Agent78coding agent18web agent3Browser and computer use4Multi-agent systems5Embodied systems and robotics6

Layer

Model layer0Application layer5
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.
Model risks：Dangerous agent actionsSystem type：Multi-agent systemsLayer：Model layerLayer：Application layerClear all

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  1. Evaluation and benchmarksarXiv 2610.06748

    BazaarBench：评估 LLM Agent 在去中心化 C2C 市场中的代理安全

    构建 BazaarBench，评测 C2C 市场中 Agent 的违规与失信行为

    arXiv
    2610.06748
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    GPT-5.5, GPT-5.4, GPT-5.4-mini and others5
    Abstract论文提出挂钩底层资产的 C2C 交易评测基准，发现时间压力与对抗指令显著推高了智能体的违规承诺与欺诈行为。

    BazaarBench 针对大语言模型智能体在去中心化 C2C 交易中的代理安全问题，提出了一个结合底层实体状态与大模型裁判的仿真基准平台。

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  4. Evaluation and benchmarksarXiv 2609.05591

    WolfSociety：金融智能体社会中有害智能体规模带来的集体风险

    提出 WolfBench，测量金融智能体社会中有害智能体规模带来的集体崩溃

    arXiv
    2609.05591
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    DeepSeek V3.2, GPT-4.1, Qwen3-235B and others6
    Abstract更大的金融智能体社会在更低有害比例上崩溃，但所需有害人数仍增加。

    WolfSociety 把多智能体安全写成受控金融社会中的集体失败问题：智能体经社交网络交流，并在共享市场里交易，形成从交流到行动再到后续决策的闭环。

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  5. Evaluation and benchmarksarXiv 2608.07556

    MasDrift：跨多智能体架构的授权保持基准

    提出 MasDrift，评测多智能体在良性任务中的授权保持

    arXiv
    2608.07556
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    DeepSeek V4 Flash, Qwen3.7 Plus, GPT-5.4 Nano and others6
    Abstract授权保持可被测量：层级用未授权动作换完成率，重锚定比沿链携带更稳。

    MasDrift 把多智能体设计中的授权保持做成可测量性质，对象是没有攻击者的普通委托。

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