Fast domain adaptation remains a fundamental challenge for deploying multi-agent systems across diverse environments in Vehicle-to-Everything (V2X) collaborative perception. Despite the success of Parameter-Efficient Fine-Tuning (PEFT) in natural language processing and conventional vision tasks, directly applying PEFT to multi-agent settings leads to significant performance degradation and training instability. In this work, we conduct a detailed analysis and identify two key factors: (i) inter-frame redundancy in heterogeneous sensory streams, and (ii) erosion of fine-grained semantics in deep-layer representations under PEFT adaptation. To address these issues, we propose FlowAdapt, a parameter-efficient framework grounded in optimal transport theory, which minimizes information transport costs across both data distributions and network hierarchies. Specifically, we introduce a Wasserstein Greedy Sampling strategy to selectively filter redundant samples via a bounded covering radius. Furthermore, Progressive Knowledge Transfer module is designed to progressively inject compressed early-stage representations into later stages through learnable pathways, alleviating semantic degradation in late-stage adaptation. Extensive experiments on three benchmarks demonstrate that FlowAdapt achieves state-of-the-art performance with only 1% of trainable parameters, effectively bridging domain gaps with superior sample efficiency and generalization.

翻译：快速域自适应仍然是车联网（V2X）协同感知中，将多智能体系统部署于多样化环境时面临的一个根本性挑战。尽管参数高效微调（PEFT）在自然语言处理和传统视觉任务中取得了成功，但将其直接应用于多智能体场景会导致显著的性能下降和训练不稳定性。在本工作中，我们进行了详细分析，并识别出两个关键因素：（i）异构传感器流中的帧间冗余，以及（ii）在PEFT自适应过程中，深层表征中细粒度语义的侵蚀。为了解决这些问题，我们提出了FlowAdapt，一个基于最优传输理论的参数高效框架，该框架最小化了跨数据分布和网络层次的信息传输成本。具体而言，我们引入了一种Wasserstein贪婪采样策略，通过一个有界覆盖半径来选择性过滤冗余样本。此外，我们设计了渐进式知识传递模块，通过可学习的路径将压缩的早期表征逐步注入到后期阶段，从而缓解后期自适应中的语义退化问题。在三个基准数据集上的大量实验表明，FlowAdapt仅使用1%的可训练参数就实现了最先进的性能，以卓越的样本效率和泛化能力有效地弥合了领域差距。