标题： GeoAda：使用等变适配器高效微调几何扩散模型 显示英文标题

标题： GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters

摘要： 几何扩散模型在分子动力学和结构生成中表现出显著的成功。然而，对于具有不同几何控制的下游任务，高效微调它们仍然研究不足。在本工作中，我们提出了一种SE(3)-等变适配器框架（GeoAda），该框架能够在不修改原始模型架构的情况下，实现可控生成任务的灵活且参数高效的微调。GeoAda引入了一种结构化的适配器设计：控制信号首先通过耦合算子进行编码，然后由可训练的选定预训练模型层副本进行处理，最后通过解耦算子投影回原空间，并随后进行等变零初始化卷积。通过仅微调这些轻量级适配器模块，GeoAda保持了模型的几何一致性，同时减轻了过拟合和灾难性遗忘。我们理论证明了所提出的适配器保持SE(3)-等变性，确保预训练扩散模型的几何归纳偏置在适应过程中保持完整。我们在多种几何控制类型上展示了GeoAda的广泛应用性，包括框架控制、全局控制、子图控制以及广泛的领域应用，如粒子动力学、分子动力学、人体运动预测和分子生成。实验结果表明，GeoAda在保持原始任务准确性的同时实现了最先进的微调性能，而其他基线方法由于过拟合和灾难性遗忘导致性能显著下降。 显示英文摘要

摘要： Geometric diffusion models have shown remarkable success in molecular dynamics and structure generation. However, efficiently fine-tuning them for downstream tasks with varying geometric controls remains underexplored. In this work, we propose an SE(3)-equivariant adapter framework ( GeoAda) that enables flexible and parameter-efficient fine-tuning for controlled generative tasks without modifying the original model architecture. GeoAda introduces a structured adapter design: control signals are first encoded through coupling operators, then processed by a trainable copy of selected pretrained model layers, and finally projected back via decoupling operators followed by an equivariant zero-initialized convolution. By fine-tuning only these lightweight adapter modules, GeoAda preserves the model's geometric consistency while mitigating overfitting and catastrophic forgetting. We theoretically prove that the proposed adapters maintain SE(3)-equivariance, ensuring that the geometric inductive biases of the pretrained diffusion model remain intact during adaptation. We demonstrate the wide applicability of GeoAda across diverse geometric control types, including frame control, global control, subgraph control, and a broad range of application domains such as particle dynamics, molecular dynamics, human motion prediction, and molecule generation. Empirical results show that GeoAda achieves state-of-the-art fine-tuning performance while preserving original task accuracy, whereas other baselines experience significant performance degradation due to overfitting and catastrophic forgetting.