提出从网络数据中学习因果表征的新方法，结合图结构与因果关系建模，为复杂网络分析提供全新视角。
arXiv:2509.01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear int…
论文提出一种通过链发现从文本中构建隐式因果图的新方法，为自然语言因果推理提供新思路。
arXiv:2606.07525v1 Announce Type: new Abstract: Causal graphs in text are typically populated by observable, predefined events. In contrast, we study …
用因果分析揭示大模型对中间推理结构的忠实度，为提升AI可信度提供新视角。
arXiv:2603.16475v2 Announce Type: replace Abstract: In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubri…
首次探索如何利用基础模型推动因果生成建模，为新范式研究提供理论基石。
arXiv:2605.23861v1 Announce Type: cross Abstract: Causal generative modeling is essential for developing reliable and transparent AI systems capable o…
探索因果关系与条件依赖的掩盖机制，为因果推断理论提供新见解
arXiv:2603.06984v2 Announce Type: replace-cross Abstract: Many regulatory and analytic problems require that a prohibited variable influence a decisio…