[D] Is GNN or large graph model promising for an interpretable knowledge-intensive system?

I am always wondering how to reuse the learned knowledge by some deep models. Seq-In-Seq-Out paradigms like LLMs put heavy constraints on LLM applications, such as automated theorem proving (now mostly fulfilled by symbolic regression), spatial relation understanding (partially captured by LLM but in a sequence pattern way), arithmetic calculation (to meet simple scenario, in a similar way of spatial relations) etc.

Recent Nature MI publishes a promising work on multimodal learning with graph model, where heterogeneous data are integrated …

applications automated constraints graph knowledge llm llms machinelearning regression theorem understanding