Graph neural networks on assurance case graphs reach 0.76 ROC-AUC for link prediction and 0.94 F1 for distinguishing human from LLM-generated cases, with observed differences in hierarchical linking patterns.
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MMP-Refer augments LLMs with multimodal retrieval paths and a trainable collaborative adapter to produce more accurate and explainable recommendations.
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
Inductive subgraphs serve as shortcuts in heterophilic graphs, and CD-GNN disentangles spurious from causal subgraphs by blocking non-causal paths to improve robustness and accuracy.
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Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
Graph neural networks on assurance case graphs reach 0.76 ROC-AUC for link prediction and 0.94 F1 for distinguishing human from LLM-generated cases, with observed differences in hierarchical linking patterns.
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MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation
MMP-Refer augments LLMs with multimodal retrieval paths and a trainable collaborative adapter to produce more accurate and explainable recommendations.
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Towards Systematic Generalization for Power Grid Optimization Problems
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
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Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Inductive subgraphs serve as shortcuts in heterophilic graphs, and CD-GNN disentangles spurious from causal subgraphs by blocking non-causal paths to improve robustness and accuracy.