CSI-Agent：用于跨域Wi-Fi CSI感知的LLM辅助少样本自适应
CSI-Agent: LLM-Assisted Few-Shot Adaptation for Cross-Domain Wi-Fi CSI Sensing
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中文总结 AI 辅助
CSI-Agent将跨域Wi-Fi CSI感知自适应转化为部署时决策问题，通过LLM规划器结合类别级证据与默认策略，在少样本下显著提升目标域性能。
中文摘要 AI 辅助
Wi-Fi信道状态信息（CSI）已实现了诸如人体活动识别等无设备感知应用。然而，CSI感知模型在跨域部署中仍然脆弱，用户或环境的变化可能导致错误的预测。现有解决方案通常将此问题视为离线模型设计问题，通过预训练更强的表示或将一种固定的自适应方法应用于整个目标域。在实践中，标记的目标数据稀缺，且在同一域偏移下，不同类别可能以不同方式失败。为解决此问题，我们提出了CSI-Agent，一种寻求证据的LLM代理，将跨域CSI自适应重新表述为部署时的决策问题。CSI-Agent不处理原始CSI或进行样本级预测，而是将目标域行为总结为感知基础的类别级证据。它从互补的CSI视角建立强大的目标自适应默认值，并使用LLM规划器确定每个类别应保留默认值还是调用专门操作。确定性验证和受限执行进一步减少了不可靠的干预。我们在四个公开数据集上评估了CSI-Agent，使用五种跨域分割，涵盖设备、用户、环境和组合偏移。在1-shot自适应下，CSI-Agent在所有分割中取得了最佳的目标域性能，并将平均Macro-F1比最强基线方法提高了约16%。
英文摘要
Wi-Fi channel state information (CSI) has enabled device-free sensing applications such as human activity recognition. However, CSI sensing models remain brittle in cross-domain deployment, where changes in users or environments can produce incorrect predictions. Existing solutions usually treat this problem as an offline model-design problem, by pretraining a stronger representation or applying one fixed adaptation method to the entire target domain. In practice, labeled target data are scarce and different classes may fail in different ways under the same domain shift. To address this, we propose CSI-Agent, an evidence-seeking LLM agent that reformulates cross-domain CSI adaptation as a deployment-time decision-making problem. Rather than processing raw CSI or making sample-level predictions, CSI-Agent summarizes target-domain behavior into sensing-grounded class-level evidence. It establishes a strong target-adaptive default from complementary CSI views and uses an LLM planner to determine whether each class should retain the default or invoke a specialized action. Deterministic verification and bounded execution further reduce unreliable interventions. We evaluate CSI-Agent on four public datasets using five cross-domain splits covering device, user, environment, and compositional shifts. Under 1-shot adaptation, CSI-Agent achieves the best target-domain performance across all splits and improves the average Macro-F1 by about 16\% compared to the strongest baseline method.
