The double empathy problem frames communication difficulties between neurodivergent and neurotypical individuals as arising from mutual misunderstanding, yet most interventions focus on autistic individuals. We present NeuroWise, a multi-agent LLM-based coaching system that supports neurotypical users through stress visualization, interpretation of internal experiences, and contextual guidance. In a between-subjects study (N=30), NeuroWise was rated as helpful by all participants and showed a significant condition-time effect on deficit-based attributions (p=0.02): NeuroWise users reduced deficit framing, while baseline users shifted toward blaming autistic "deficits" after difficult interactions. NeuroWise users also completed conversations more efficiently (37% fewer turns, p=0.03). These findings suggest that AI-based interpretation can support attributional change by helping users recognize communication challenges as mutual.

翻译：双重共情问题将神经多样性个体与神经典型个体之间的沟通困难归因于相互误解，然而大多数干预措施都聚焦于自闭症个体。我们提出了NeuroWise，一个基于多智能体大语言模型的辅导系统，通过压力可视化、内部体验解读和情境指导来支持神经典型用户。在一项被试间研究（N=30）中，所有参与者均认为NeuroWise有帮助，并在缺陷归因上显示出显著的“条件-时间”效应（p=0.02）：NeuroWise用户减少了缺陷归因框架，而基线用户在困难互动后更倾向于指责自闭症“缺陷”。NeuroWise用户还以更高的效率完成了对话（轮次减少37%，p=0.03）。这些发现表明，基于人工智能的解读可以通过帮助用户认识到沟通挑战是相互的，从而支持归因改变。