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LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

arXiv cs.AI Structured Data AI Xingxuan Zhang, Gang Ren, Hao Yuan, Hao Zou, Hongze Tan, Hui Wang, Jianhao Song, Jiansheng Li, Jiayao Zhang, Jinghan Zhang, Kaifang Li, Lang Mo, Li Mao, Mingchao Hao, Nuo Xu, Rui Ding, Ruiji Zhang, Shuyang Li, Siyu Mei, Tianyang Zhang, Weiyang Mu, Yancheng Dong, Yongxian Wei, Yuan Xue, Yuanrui Wang, Yue He, Zijia Yang, Ziyun Li, Dongzhe Li, Fuqiang Wang, Jiandong Liu, Jiawei Chen, Jiaxin Du, Kaijie Cheng, Kehan Li, Lei Sun, Linjun Zhou, Ningbo Dai, Qi Wang, Renzhe Xu, Shaoxing Du, Shumeng Yang, Wang Lu, Wenjing Chu, Xiannan Huang, Xiaoyu Lin, Xing Ai, Xinyan Han, Xuanyue Li, Xuanyue Su, Xukun Zhang, Yan Lu, Yaxin Zhang, Yi Qin, Yifei Huang, Yihan Xu, Yongle Lv, Yuanyuan Jiang, Yushan Han, Peng Cui 2026-09-15

TL;DR - LimiX-2 is a foundation model for structured data that jointly models context-dependent data-generating mechanisms rather than focusing only on target prediction. It reportedly outperforms dataset-specific and tabular foundation models while also supporting causal skeleton recovery.

Uses Contextual Mechanism Networks to learn (p(x, y \mid D_{\mathrm{context}})), shifting in-context learning toward joint mechanism modeling.
Pretrains via Context-Conditional Masked Modeling on synthetic datasets generated by diverse structural causal models.
Evaluations on TabArena, TALENT, and BCCO show gains over existing dataset-specific models and tabular foundation models.
Feature attention captures direct causal relationships, enabling accurate recovery of causal graph skeletons.

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