做论证推理或AI对齐的团队会感兴趣——CDAFs让智能体通过选择激活哪些视角来操控论证结果，比传统框架更贴近现实博弈，值得关注其复杂度界限和未来多智能体扩展。
论文提出上下文依赖论证框架（CDAFs），扩展了Dung的论证理论，允许根据外部环境（上下文）动态决定哪些攻击有效。通过视角标记的特殊化，从相关性集合和优先级推导出击败函数，其中相关性集合是智能体的行动空间。在小型示例中，智能体的目标论证在完全相关注入优先级下被拒绝，但在部分激活下被接受，且部分激活无法被任何VAF受众镜像。定义了对应的决策问题ACTIVATION-MANIPULATION，并给出了基线复杂度界限。该工作为理解智能体如何通过选择激活视角来战略性地影响论证结果提供了新框架。
Choosing the Lens: Strategic Perspective Activation in Context-Dependent Argumentation
The same arguments often need to be evaluated under different external regimes. An agent with influence over the regime has a strategic lever that standard formalisms do not directly capture. We introduce context-dependent argumentation frameworks (CDAFs), an extension of Dung's theory in which a defeat function determines, per context, which attacks succeed. A perspective-labeled specialisation derives the defeat function from a relevance set $ρ$ and a priority $π$. The relevance set is the agent's action space. In a small worked example, the agent's target argument is rejected under every full-relevance injective priority, yet accepted under partial activations, one of which no VAF audience can mirror. We define the corresponding decision problem, ACTIVATION-MANIPULATION, and record baseline complexity bounds. Tight bounds and multi-agent variants are left open.