The paper proposes a hierarchical graph neural network (GNN) architecture for multi‑grid power‑flow modeling that exchanges information through two reduced graphs within a GENCO‑based corrective network. Experiments on three grid topologies show that the Kron‑derived transport approach reduces macro family‑balanced voltage error by 85 % compared to a flat backbone and 31 % compared to a Quotient construction, outperforming per‑bus mean baselines across all seeds. Preliminary results indicate strong generalization across operating scenarios within the studied topologies, though cross‑topology transfer remains an open challenge.
By Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias, Juan Pablo Maroto Herrera, Ane Miren Florez-Tapia, Marco Quartulli. Izaro Goienetxea Urziku
arXiv:2606. 24722v1 Announce Type: new Abstract: Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters.
By Peter Toth
arXiv:2605. 20982v2 Announce Type: replace-cross Abstract: AlltoAll dispatch is the dominant bottleneck of MoE expert parallelism, and the interconnect community has responded with four families of mitigations: predictive sample placement, adaptive expert relayout, hierarchical collectives, and EP-aware topology.
By Bole Ma, Jan Eitzinger, Harald Koestler, Gerhard Wellein
arXiv:2609.37899v1 Announce Type: new
Abstract: Zero-order optimization (ZO) trains without backpropagation, making it relevant to forward-only hardware and non-differentiable loss, but its gradient...
By Francois Chaubard, Mykel J. Kochenderfer, Chris R\'e
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.
arXiv:2607. 13763v1 Announce Type: cross Abstract: 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.
By Charilaos Papaioannou, Ioannis Tsantilas, Dimitris Giannakakos, Vasilis Michalakopoulos, Sotiris Pelekis, Vangelis Marinakis, Arsam Aryandoust, Antonello Monti, Ricardo J. Bessa, Perdo P. Vergara, Jochen Cremer, Elissaios Sarmas