无需数据即可让物联网模型自适应环境变化，性能提升超20%。

A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes in sensor placement or ambient environments, can substantially alter sensing patterns and degrade model performance. We present Chorus, a context-bridged, data-free post-deployment model customization approach that adapts sensing models to unseen contexts without requiring target-domain sensor data or post-deployment retraining. Chorus learns compact, transferable context representations and aligns them with the sensor latent space using unlabeled sensor-context pairs, bridging context generalization with sensing-data generalization. It then uses a lightweight gated prediction head to integrate context priors at inference and an adaptive caching mechanism to reuse context representations when no context shift is detected, reducing on-device overhead. Experiments on IMU sensing, speech enhancement, and WiFi sensing under diverse context shifts show that Chorus outperforms state-of-the-art baselines by up to 20.2% in unseen contexts, achieves inference latency comparable to sensor-only deployment, and remains stable under continuous context transitions and varied context descriptions. A video demonstration is available at https://youtu.be/yANTZsk0TVU.