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Oct 13 · Tuesday

All papers

Found 43 papers

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  1. Reward hacking as a transmissible strategy in multi-agent coding systems

    Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems

    Published
    Evaluated models
    Qwen2.5-Coder-14B, Gemma-3-12B, Qwen2.5-Coder-7B and others16
    AbstractThe authors state that reward hacking can spread across models and affect routing and aggregation, and that review can reduce delivery.

    Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.

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  2. 审计发现 CLIP 安全分数主要反映画面与提示词场景的相似度

    开环审计冻结视觉语言安全分,发现其反映场景模板而非危险感知

    Published
    Evaluated models
    CLIP ViT-B/32, CLIP ViT-L/14, Qwen2-VL-7B
    Abstract论文审计了冻结 VLM 安全评分机制,指出其反映场景模板而非危险感知,为安全强化学习评估提供了严谨基准。

    本文是一项针对安全强化学习中冻结视觉语言模型(VLM)安全评分机制的实证审计研究。

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  3. 论文提出 agent plasticity 指标衡量智能体通过经验自我改进的效率

    提出 agent plasticity 指标衡量智能体通过经验自我改进的效率

    Published
    Evaluated models
    Claude Fable 5, Claude Opus 5, GPT-5.6 Sol and others10
    Abstract作者用固定权重协议比较前沿模型把经验写成制品后的留出增益与习得效率。

    作者用 agent plasticity 衡量一件事:权重冻结时,智能体把经验写成可继承制品,再换成留出能力增益,这个转换有多省。

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  4. DefensesarXiv 2610.05241

    StateWise: Diagnosing and repairing persistent operation state before agent actions

    Propose StateWise, which diagnoses and repairs invalid persistent state before coding agent actions

    Published
    Evaluated models
    GPT-5.6-sol, DeepSeek v4 Pro, Qwen 3.7 Max and others4
    AbstractStateWise repairs stale persistent premises before action.

    StateWise is a framework that diagnoses and repairs the persistent operational state of coding agents before executing actions with side effects.

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  5. DefensesarXiv 2610.04616

    PerturBot 用扰动训练打破视觉-语言-动作模型的模态捷径

    提出 Perturbot 扰动训练,打破 VLA 的模态捷径

    Published
    Evaluated models
    π0.5
    AbstractPerturbot 改训练数据以削弱三类模态捷径,GroundFscore 用来看成功是否来自任务证据。

    Perturbot 是一套不改推理的 VLA 训练期数据干预,用来削弱模态捷径,并用离线分数区分“任务变好”和“靠捷径变好”。

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  6. DefensesarXiv 2610.03089

    COBRA: A Dual-LLM Architecture for Defending Computer-Using Agents Against Branch-Jumping Attacks

    提出 COBRA 双 LLM 架构,防御计算机使用 Agent 的分支转向攻击

    Published
    Evaluated models
    gpt-5, UI-TARS-1.5-7B, claude-sonnet-5 and others6

    Abstract提出分支转向防御架构 COBRA,通过事前能力约束与边界代理消除分支转向攻击并兼顾效用。

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  7. DefensesarXiv 2609.38983

    The paper proposes an Approval Laundering taxonomy and empirically shows that Claude Code's approval and execution binding fails

    形式化编程 Agent 的审批洗白并提出 HMAC 权能令牌防御

    Published
    Evaluated models
    Claude Code (v2.1.197, opus[1m] / claude-sonnet-5-ccmax), Codex CLI (@openai/codex v0.154.0)

    Abstract系统揭示编程智能体审批洗白风险,提出七字段权能防御机制,证实其能消除身份时序偏离但难防副作用偏离。

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  8. The study measures and predicts refusal variability across tasks in embodied VLM planners

    测量并预测具身 VLM 规划器的任务拒答可变性

    Published
    Evaluated models
    gemini-robotics-er-2-preview, gemini-robotics-er-1.6-preview
    Abstract日常物体能翻转一部分已给出的拒答,可翻转程度取决于任务且可被本地代理预测。

    作者测量 constitution 守护的具身 VLM 规划器:一次拒答在只往场景里放一个日常物体之后还保不保持得住,并把这种易翻转程度称为任务的 malleability。

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  9. DrivingBench 发布:让视觉语言模型驾驶丰田 Corolla 绕桩

    提出 DrivingBench,评测通用视觉语言模型能否闭环驾驶真车

    Published
    Evaluated models
    GPT-6 Astra, Claude Fable 5.1, Grok 4.6 and others4
    Abstract仅 Astra 第二次跑完低速锥桶路线。

    DrivingBench 评测通用 VLM 能否自己闭环开真车,推理延迟也算进任务。

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  10. DIBench:面向移动 GUI 智能体多候选选择决策完整性的安全基准

    用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性

    Published
    Evaluated models
    Qwen2.5-VL(32B), Qwen3-VL(32B), GUI-Owl(32B) and others7

    Abstract论文提出移动智能体决策完整性基准 DIBench,发现欺诈 UI 注入诱导目标选择并推高完成率,通用防御收益不稳定。

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  11. DefensesarXiv 2609.36170

    Assay: Using Content-Addressed Evidence Graphs to Constrain Invalidation of AI Coding Agents' Claims

    提出 Assay,用依赖锥哈希绑定代码断言并在合并门禁拦截失效声明

    Published
    AbstractAssay 提供随依赖内容失效的证据管理。

    Assay 是一套面向 AI 辅助软件交付的内容寻址证据系统,关注代码改变后,测试通过、无秘密或行为保持等断言还能否用于批准合并。

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