Tuning-Free Causal AI Across Dimensions

Modern artificial intelligence systems process 1D text, 2D images, 3D point clouds, and high-dimensional sensor arrays, yet existing causal methods cannot unify reasoning across such dimensional diversity. We introduce a dimension-agnostic causal inference framework to address this fundamental challenge in multimodal AI. It advances three core innovations: Dmax centric data standardization, dimension derived first principles inference, and a self assessing stability boundary. Applied to 108 real-world causal pairs spanning 2D to 32D (214,680 samples), our framework achieves 81.48% accuracy within its theoretically reliable range—significantly outperforming 10 benchmark methods (best alternative: 59.26%, p=0.017) while providing quantified reliability estimates. This framework enables unified reasoning across dimensional modalities—providing a trustworthy foundation for high-stakes applications and representing a pivotal advance toward interpretable, reliable AI.