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Oct 12 · Monday

All papers

Found 2 papers
Filter3

Model risks

Dangerous agent actions2

Defense type

Model hardening3Inference-time intervention2

System type

AI Agent3Embodied systems and robotics2
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Model risks：Dangerous agent actionsDefense type：Inference-time interventionSystem type：Embodied systems and roboticsClear all

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  1. DefensesarXiv 2610.12432

    FAITH: A feasibility-aware safety filtering reinforcement learning framework for high-dimensional systems

    Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints

    arXiv
    2610.12432
    Published
    Layer
    Training and data layer
    Setting
    Embodied systems and robotics
    AbstractFAITH separates safety filtering from reward optimization.

    FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.

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  2. DefensesarXiv 2610.05166

    安全动作不等于可行动作：VICS-G 为 VLA 策略做可行未来解码

    提出 VICS-G 可行未来解码，重排冻结 VLA 策略的动作候选

    arXiv
    2610.05166
    Published
    Layer
    Model layer
    Setting
    Embodied systems and robotics
    Evaluated models
    SafeVLA (task-optimized / base), SafeVLA (safety-aligned / safe)
    Abstract作者用可行未来解码重排冻结 VLA 的下一步，并称 VICS-G 降低观测代价且完成率接近策略采样。

    训练无关解码器 VICS 在冻结 VLA 的推理阶段重排动作块，使当前选择带上该动作仍可能留下的安全完成质量。

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