Paper Detail
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
We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mechanism Networks (CMNs) paradigm and is pretrained with Context-Conditional Masked Modeling (CCMM). CMNs shifts the organizing principle of in-context learning from target-centric prediction to mechanism-oriented joint modeling. Rather than centering the network on the p(y mid x, D_{context}) objective of conventional tabular PFNs, it is designed around learning p(x, y mid D_{context}), a context-dependent representation of the joint structure underlying data generation. Pretraining uses synthetic datasets generated by structural causal models (SCMs) spanning diverse graph structures, functional mechanisms, and observation processes. Evaluations on TabArena, TALENT, and BCCO show that LimiX-2 outperforms current dataset-specific models and tabular foundation models. Beyond predictive performance, the CMN paradigm also promotes causal awareness in LimiX-2: its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.
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@misc{zhang2026limix,
title = {LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence},
author = {Xingxuan Zhang and Gang Ren and Hao Yuan and Hao Zou and Hongze Tan and Hui Wang and Jianhao Song and Jiansheng Li and Jiayao Zhang and Jinghan Zhang and Kaifang Li and Lang Mo and Li Mao and Mingchao Hao and Nuo Xu and Rui Ding and Ruiji Zhang and Shuyang Li and Siyu Mei and Tianyang Zhang and Weiyang Mu and Yancheng Dong and Yongxian Wei and Yuan Xue and Yuanrui Wang and Yue He and Zijia Yang and Ziyun Li and Dongzhe Li and Fuqiang Wang and Jiandong Liu and Jiawei Chen and Jiaxin Du and Kaijie Cheng and Kehan Li and Lei Sun and Linjun Zhou and Ningbo Dai and Qi Wang and Renzhe Xu and Shaoxing Du and Shumeng Yang and Wang Lu and Wenjing Chu and Xiannan Huang and Xiaoyu Lin and Xing Ai and Xinyan Han and Xuanyue Li and Xuanyue Su and Xukun Zhang and Yan Lu and Yaxin Zhang and Yi Qin and Yifei Huang and Yihan Xu and Yongle Lv and Yuanyuan Jiang and Yushan Han and Peng Cui},
year = {2026},
abstract = {We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mechanism Networks (CMNs) paradigm and is pretrained with Context-Conditional Masked Modeling (CCMM). CMNs shifts the organizing principle of in-context learning from target-centric prediction to mechanism-oriented joint modeling. Rather than centering the network on the p(y mid x, D\_\{context\}) objective of conventional t},
url = {https://huggingface.co/papers/2609.17488},
keywords = {code available, huggingface daily},
eprint = {2609.17488},
archiveprefix = {arXiv},
}
{}