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Proceedings of the AAAI Conference on Artificial Intelligence

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Magnitude-Modulated Equivariant Adapter for Parameter-Eff...
Dian Jin, Ya · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Dian Jin Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University
  • Yancheng Yuan Department of Applied Mathematics, The Hong Kong Polytechnic University
  • Xiaoming Tao Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University

DOI:

https://doi.org/10.1609/aaai.v40i1.37013

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.

How to Cite

Jin, D., Yuan, Y., & Tao, X. (2026). Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 498–506. https://doi.org/10.1609/aaai.v40i1.37013

Issue

Section

AAAI Technical Track on Application Domains I

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