立场：可解释性研究必须优先考虑基础而非临时方法

arXiv:2607.14123v1 Announce Type: new Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows.在实践中，它们常常在没有指导有意义的行动的情况下产生和丢弃。这一差距反映了根本性的缺陷：研究尚未建立将解释整合到端到端、人机交互系统中的方法。 This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress.最后，我们概述了一个实用的清单，旨在将 XAI 转向更加以人为中心、以行动为导向的范式。 By emphasizing foundational clarity over the development of ad-hoc methods, we hope to provide a roadmap for integrating explanations into actionable, feedback-driven AI systems.