ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a policy-grounded decision layer and end-to-end zero-knowledge proofs of inference. The encoder uses masked spectral pretraining with phase-consistency regularization, plus a light cross-modal alignment that ties RF features to compact, human-interpretable policy tokens. To reduce unsafe actions under distribution shift, we add a calibrated selective-abstention head; the chosen risk-coverage operating point is registered and bound into the proof. We implement a four-stage proving pipeline: (C1) feature sanity and commitment, (C2) threshold and version binding, (C3) time-window binding, and (C4) PLONK-style proofs that the quantized network, given the committed window, produced the logged action and confidence. Micro-batched proving amortizes cost across adjacent windows, and a gateway option offloads proofs from low-power devices. The system integrates with differentially private federated learning and on-device personalization without weakening verifiability: model hashes and the registered threshold are part of each public statement. Across activity, presence or intrusion, respiratory proxy, and RF fingerprinting tasks, ZK-SenseLM improves macro-F1 and calibration, yields favorable coverage-risk curves under perturbations, and rejects tamper and replay with compact proofs and fast verification.

翻译：ZK-SenseLM 是一个安全且可审计的无线感知框架，它将用于Wi-Fi信道状态信息（及可选的毫米波雷达或RFID）的大模型编码器，与一个基于策略的决策层以及端到端的推理零知识证明相结合。该编码器采用带相位一致性正则化的掩码频谱预训练，并辅以轻量级跨模态对齐，将射频特征映射到紧凑、人类可理解的策略令牌。为减少分布漂移下的不安全行为，我们增加了一个经过校准的选择性弃权头；所选的风险-覆盖工作点会被注册并绑定到证明中。我们实现了一个四阶段的证明生成流程：（C1）特征完整性检查与承诺，（C2）阈值与版本绑定，（C3）时间窗口绑定，以及（C4）PLONK式证明，用于证明量化网络在给定承诺窗口下产生了所记录的动作及置信度。微批量证明将成本分摊到相邻窗口，且网关选项可将证明任务从低功耗设备上卸载。该系统可与差分隐私联邦学习及设备端个性化集成，同时不削弱可验证性：模型哈希值和注册的阈值是每个公开声明的一部分。在活动识别、存在或入侵检测、呼吸代理以及射频指纹识别等任务中，ZK-SenseLM 提升了宏平均F1分数与校准性能，在扰动下呈现出有利的覆盖-风险曲线，并能通过紧凑的证明和快速验证抵御篡改与重放攻击。