Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks

• URL: http://arxiv.org/abs/2511.06696v1
• Date: Mon, 10 Nov 2025 04:31:56 GMT
• Title: Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks
• Authors: Dian Jin, Yancheng Yuan, Xiaoming Tao,
• Abstract summary: Magnitude-Modulated Equivariant Adapter (MMEA)<n>We present a novel equivariant fine-tuning method which employs lightweight scalar gating to modulate feature magnitudes on a per-order and per-multiplicity basis.<n>Results suggest that, in many practical scenarios, modulating channel magnitudes is sufficient to adapt equivariant models to new chemical environments without breaking symmetry.
• Score: 17.452647514694004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained equivariant graph neural networks based on spherical harmonics offer efficient and accurate alternatives to computationally expensive ab-initio methods, yet adapting them to new tasks and chemical environments still requires fine-tuning. Conventional parameter-efficient fine-tuning (PEFT) techniques, such as Adapters and LoRA, typically break symmetry, making them incompatible with those equivariant architectures. ELoRA, recently proposed, is the first equivariant PEFT method. It achieves improved parameter efficiency and performance on many benchmarks. However, the relatively high degrees of freedom it retains within each tensor order can still perturb pretrained feature distributions and ultimately degrade performance. To address this, we present Magnitude-Modulated Equivariant Adapter (MMEA), a novel equivariant fine-tuning method which employs lightweight scalar gating to modulate feature magnitudes on a per-order and per-multiplicity basis. We demonstrate that MMEA preserves strict equivariance and, across multiple benchmarks, consistently improves energy and force predictions to state-of-the-art levels while training fewer parameters than competing approaches. These results suggest that, in many practical scenarios, modulating channel magnitudes is sufficient to adapt equivariant models to new chemical environments without breaking symmetry, pointing toward a new paradigm for equivariant PEFT design.

Related papers

• Variational Entropic Optimal Transport [67.76725267984578]
  We propose Variational Entropic Optimal Transport (VarEOT) for domain translation problems.<n>VarEOT is based on an exact variational reformulation of the log-partition $log mathbbE[exp(cdot)$ as a tractable generalization over an auxiliary positive normalizer.<n> Experiments on synthetic data and unpaired image-to-image translation demonstrate competitive or improved translation quality.
  arXiv  Detail & Related papers  (2026-02-02T15:48:44Z)
• Symmetry Breaking in Transformers for Efficient and Interpretable Training [5.624886369964602]
  We introduce a simple symmetry-breaking protocol that inserts a preferred direction into a rotational space through batchwise-sampled, unlearned query and value biases.<n>First, it can substantially improve the performance of simple, memory-efficients.<n>Second, it enables an interpretable use of otherwise redundant rotational degrees of freedom.
  arXiv  Detail & Related papers  (2026-01-29T19:29:09Z)
• Learning (Approximately) Equivariant Networks via Constrained Optimization [25.51476313302483]
  Equivariant neural networks are designed to respect symmetries through their architecture.<n>Real-world data often departs from perfect symmetry because of noise, structural variation, measurement bias, or other symmetry-breaking effects.<n>We introduce Adaptive Constrained Equivariance (ACE), a constrained optimization approach that starts with a flexible, non-equivariant model.
  arXiv  Detail & Related papers  (2025-05-19T18:08:09Z)
• A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures [53.273077346444886]
  Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials.<n>A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures.<n>HIENet is a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers.
  arXiv  Detail & Related papers  (2025-02-25T18:01:05Z)
• Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion [55.95767828747407]
  In domains such as molecular and protein generation, physical systems exhibit inherent symmetries that are critical to model.<n>We present a framework that reduces training variance and provides a provably lower-variance gradient estimator.<n>We also present a practical implementation of this estimator incorporating the loss and sampling procedure through a method we call Orbit Diffusion.
  arXiv  Detail & Related papers  (2025-02-14T03:26:57Z)
• Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning [41.19870454097444]
  We propose a novel approach that models network weights by leveraging a combination of physical priors.<n>We use three foundational equations -- heat diffusion, wave propagation, and Poisson's steady-state equation -- each contributing distinctive modeling properties.<n>MoPPA improves PEFT accuracy by up to 2.1% on VTAB-1K image classification with a comparable number of trainable parameters.
  arXiv  Detail & Related papers  (2024-12-03T19:00:34Z)
• Improving Equivariant Model Training via Constraint Relaxation [31.507956579770088]
  We propose a novel framework for improving the optimization of such models by relaxing the hard equivariance constraint during training.<n>We provide experimental results on different state-of-the-art network architectures, demonstrating how this training framework can result in equivariant models with improved generalization performance.
  arXiv  Detail & Related papers  (2024-08-23T17:35:08Z)
• ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections [59.839926875976225]
  We propose the ETHER transformation family, which performs Efficient fineTuning via HypErplane Reflections. In particular, we introduce ETHER and its relaxation ETHER+, which match or outperform existing PEFT methods with significantly fewer parameters.
  arXiv  Detail & Related papers  (2024-05-30T17:26:02Z)
• Relaxing Equivariance Constraints with Non-stationary Continuous Filters [20.74154804898478]
  The proposed parameterization can be thought of as a building block to allow adjustable symmetry structure in neural networks. Compared to non-equivariant or strict-equivariant baselines, we experimentally verify that soft equivariance leads to improved performance in terms of test accuracy on CIFAR-10 and CIFAR-100 image classification tasks.
  arXiv  Detail & Related papers  (2022-04-14T18:08:36Z)
• Equivariant vector field network for many-body system modeling [65.22203086172019]
  Equivariant Vector Field Network (EVFN) is built on a novel equivariant basis and the associated scalarization and vectorization layers. We evaluate our method on predicting trajectories of simulated Newton mechanics systems with both full and partially observed data.
  arXiv  Detail & Related papers  (2021-10-26T14:26:25Z)
• Frame Averaging for Invariant and Equivariant Network Design [50.87023773850824]
  We introduce Frame Averaging (FA), a framework for adapting known (backbone) architectures to become invariant or equivariant to new symmetry types. We show that FA-based models have maximal expressive power in a broad setting. We propose a new class of universal Graph Neural Networks (GNNs), universal Euclidean motion invariant point cloud networks, and Euclidean motion invariant Message Passing (MP) GNNs.
  arXiv  Detail & Related papers  (2021-10-07T11:05:23Z)

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.