SEGN: A Philosophy-Driven Framework for Condition-Dependent AI Reasoning

In this study, the author proposes a non-essentialist structural inference modeling approach to address the limitations of traditional graph neural networks (GNNs), such as weak inference ability and insufficient interpretability, especially under missing conditions, structural perturbations, and ontological rigidity. A Structural Emptiness Graph Network (SEGN) is constructed based on the ontology reversal principle that "entities emerge with conditions." The model introduces a null response mechanism, counterfactual inference module, chain repair strategy, and latent graph memory unit to form a dynamic causal inference pathway that is rejectable, repairable, and traceable. In experiments on multi-agent path prediction and structurally incomplete graphs, SEGN achieves a 78.4% reasoning recovery rate under 30% structural perturbation, a 0.24 improvement in the XAI explanatory index, and a 15.8% reduction in the standard deviation of structural stability, significantly outperforming baseline models such as GCN and GAT. These results confirm that the structural philosophy of "emptiness and conditional manifestation" can be formally embedded into AI systems, offering a feasible interdisciplinary pathway toward building next-generation reasoning models with structural adaptability, causal transparency, and ethical rejection capabilities.