这篇论文给AI装了个'读心开关'——只在冲突场景下按需启动心智推理，既省算力又提升准确性，值得做AI安全和多智能体的人看看。
该论文构建了一个结构因果模型（DAG），将心智理论视为由情境与主体条件激活的机制，而非始终开启的能力。模型包含四个外生变量（如冲突强度、信息可及性）和五个内生中介变量，通过可处理性路径、推理深度路径和使能原因路径三种机制决定心智理论的参与状态。主要输出变量是认知准确性，该框架为AI系统提供了资源理性的心智化决策流程。论文还通过仿真验证和人类-智能体团队实验评估了模型的有效性，并讨论了冲突优化心智化引发的伦理问题。
A Causal Model of Theory of Mind in Conflict for Artificial Intelligence
Theory of mind (ToM), the capacity to ascribe mental states to others and use those ascriptions for prediction and inference, is widely assumed to be essential for effective human-machine integration. Existing AI-ToM models address \emph{how} to mentalize, but leave the question of when largely unaddressed. The central question is: under what situational and agent-level conditions is ToM engagement causally warranted in conflict? This paper presents a structural causal model formalized as a directed acyclic graph (DAG), treating ToM as a mechanism activated by situational and agent-level conditions rather than as an always-on capacity. The model specifies four exogenous variables capturing situational and agent-level conditions, five endogenous mediators, and a mechanistic ToM node producing engagement states through three distinct causal pathways: a tractability pathway, a reasoning-depth pathway, and an enabling-cause pathway. The primary outcome is epistemic accuracy, which decouples social reasoning from behavioral policy and generalizes across social phenomena beyond conflict. The framework gives AI systems a principled, resource-rational decision procedure for mentalizing, with implications for efficiency, trust, and the development of robust artificial social intelligence. Simulation validation, empirical human-machine teaming studies, and ethical considerations arising from conflict-optimized mentalizing are discussed.