MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model

• URL: http://arxiv.org/abs/2607.13763v2
• Date: Fri, 17 Jul 2026 14:27:46 GMT
• Title: MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
• Abstract summary: Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode.<n>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.<n>We introduce MxGPS (Multiplex GPS), a multiplex graph transformer that runs K task-specialised GPS branches over a shared node encoder.
• Score: 0.7951418336539415
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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 task-specialised GPS branches over a shared node encoder, jointly trained on Static State Estimation (SSE) and AC Power Flow (PF) via a self-supervised pre-training and multi-task fine-tuning protocol, with a cross-branch attention module evaluated in ablation. The joint SSE+PF objective forces the shared encoder to simultaneously satisfy complementary gradient signals, preventing it from overfitting to topology-specific relational structure. Under a 3-fold sliding-window cross-validation spanning four unseen topologies (14-, 24-, 162-, and 300-bus), MxGPS attains 0% boundary violation rate (BVR) on all four zero-shot Power Flow topologies. Critically, models with substantially lower in-distribution PF error degrade by 190% to 1400% under topology shift, whereas MxGPS degrades by only 39%, an inversion that directly implicates topology overfitting as the failure mechanism rather than insufficient model capacity. With only 1.6M parameters (12x fewer than the GridFM reference baseline), MxGPS demonstrates that multi-task joint training is a principled and parameter-efficient mechanism for topology-agnostic generalisation in power grid foundation models.

Related papers

• Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation [1.834098271561585]
  We propose a single Heterogeneous Residual Gated Graph Convolutional Network that solves all three problems with one shared backbone.<n>We trained jointly on the three problems across diverse topologies and loading conditions, and evaluated on the IEEE 14-bus and 118-bus systems.<n>These results show that a single model can capture the basic operation of a power network and serve several analysis tasks at once, a first step toward a foundation model for power systems.
  arXiv  Detail & Related papers  (2026-09-15T07:13:05Z)
• Closed-Form Spectral Regularization for Multi-Task Model Merging [96.82449201305234]
  Model merging combines several independently fine-tuned experts into a single multi-task model without any training data.<n>State-of-the-art merging methods formulate merging as a layer-wise interference problem.<n>We propose SWUDI, a closed-form method that combines a soft exponential filter, which matches the gradient-flow trajectory of iterative descent.
  arXiv  Detail & Related papers  (2026-06-05T14:00:47Z)
• Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs [69.56407708794845]
  MultimodalAttributed Graph Learning (MAGL) integrates node attributes with structural topology via graph aggregation.<n>As pretrained encoders evolve into Large Foundation Models (LFMs), the landscape of MAGL fundamentally shifts.<n>Under high-confidence LFM priors, mandatory aggregation introduces topological intrinsic noise that overwhelms discriminative signals.<n>We propose SUPRA (Shared-Unique Prior-Retaining Architecture), a decoupled dual-pathway paradigm.<n> SUPRA processes modality-specific features through topology-agnostics while capturing structural synergy via a lightweight shared GNN, with auxiliary deep supervision counteracting gradient starvation.
  arXiv  Detail & Related papers  (2026-05-23T17:42:25Z)
• Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids [7.737263980113792]
  This paper presents a scalable heterogeneous graph neural network (GNN) workflow for data-driven OPF surrogate modeling and OPF-GFM development.<n>The workflow preserves the distinct node and edge types of power grids and supports distributed preprocessing, training, and downstream fine-tuning on leadership-class supercomputers.
  arXiv  Detail & Related papers  (2026-05-22T03:25:25Z)
• Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation [0.0]
  EDA problems are graph-structured, but not all graph-structured problems call for the same GNN computation.<n>We argue that successful GNN-for-EDA methods are those whose propagation, aggregation, and supervision align with the native algebra of the target task.
  arXiv  Detail & Related papers  (2026-05-08T08:24:33Z)
• Graph Signal Diffusion Models for Wireless Resource Allocation [74.84410305593006]
  We consider constrained ergodic resource optimization in wireless networks with graph-structured interference.<n>We show that time-sharing the generated power allocations achieves near-optimal ergodic sum-rate utility and near-feasible ergodic minimum-rates.
  arXiv  Detail & Related papers  (2026-04-06T21:12:25Z)
• BOOST-RPF: Boosted Sequential Trees for Radial Power Flow [0.0]
  We introduce BOOST-RPF, a novel method that reformulates voltage prediction from a global graph regression task into a sequential path-based learning problem.<n>We evaluate three architectural variants: Absolute Voltage, Parent Residual, and Physics-Informed Residual.<n> Benchmarked against the Kerber Dorfnetz grid and the ENGAGE suite, BOOST-RPF achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2026-03-23T13:41:36Z)
• Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks [52.153950303594684]
  We propose a decoupled kinetic paradigm inspired by Alternating Gradient Flow (AGF)<n>AGF accurately captures the network's structural "kinetic utility"<n>We design a hybrid routing framework that decouples AGF-guided offline structural search from online execution via zero-cost physical priors.
  arXiv  Detail & Related papers  (2026-03-12T18:19:21Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks [4.303580795892996]
  Decoupled Graph Convolutional Network (D-GCN) is a novel architecture that explicitly separates processing of a node's own transmission probability from neighbor interference effects.<n>D-GCN attains 3.3% NMAE, outperforms strong baselines, remains tractable even when exact analytical methods become computationally infeasible.
  arXiv  Detail & Related papers  (2025-10-15T22:13:59Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.