MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance. To expose and address this failure mode, we introduce MxGPS (Multiplex GPS), a multiplex graph transformer that runs K tas
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- FuzzySimilar title/name (fuzzy) · 87%lucidrains/x-transformers →
“Fuzzy title match (0.94): “MxGPS: Multiplex Graph Transformers for a Power Grid Foundat” ≈ “lucidrains/x-transformers””
- FuzzySimilar title/name (fuzzy) · 84%huggingface/transformers →
“Fuzzy title match (0.92): “MxGPS: Multiplex Graph Transformers for a Power Grid Foundat” ≈ “huggingface/transformers””
- LinkedLinked via arxiv author · 85%Charilaos Papaioannou →
“MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model”
- LinkedLinked via arxiv author · 85%Ioannis Tsantilas →
“MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model”
- LinkedLinked via arxiv author · 85%Dimitris Giannakakos →
“MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model”
- LinkedLinked via arxiv author · 85%Vasilis Michalakopoulos →
“MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model”
- LinkedLinked via arxiv author · 85%Sotiris Pelekis →
“MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model”
- LinkedLinked via arxiv author · 85%Vangelis Marinakis →
“MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model”