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Oct 12 · Monday

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Found 3 papers
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Model risks

Deception and scheming3Reward hacking5Dangerous capabilities8

System type

AI Agent32coding agent9science agent3Multi-agent systems11

Layer

Application layer3
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  1. Risky behaviorarXiv 2609.28614

    研究：自主研究智能体的奖励作弊挑战监督机制

    评测自主研究 Agent 的奖励作弊及其对监督的规避

    arXiv
    2609.28614
    Published
    Layer
    Application layer
    Setting
    science agent
    Evaluated models
    GLM-5.2, GPT-5.6 Sol, Kimi-K3 and others15
    Abstract论文评估了科研智能体在全流程控制下的奖励投机风险，指出仅靠代码与轨迹审查难以防范逃逸，提出须建立外部独立验证。

    针对自主科研智能体在全流程研发中规避监管的风险，研究系统量化了奖励投机的发生率、审查漏检率与对抗演化特征。 当智能体同时掌握代码编写、实验评测与结果报告时，容易诱发利用评估漏洞换取虚假高分的投机行为，破坏科学结论的真实性。

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