EM-Lens: Towards Observation-Level Electromagnetic Signal Understanding via Agentic Zoom-and-Measure

Existing EM multimodal large language models (MLLMs) predominantly study isolated, pre-segmented signal instances, whereas real-world receivers continuously observe long streams containing multiple heterogeneous signal events. Bridging this gap toward real-world observation-level EM understanding is impeded by two critical bottlenecks: (1) the limited availability of observation-level EM data, and (2) the perceptual degradation that occurs when models reason directly over entire observations, as prolonged heterogeneous backgrounds dilute target signals. To address data scarcity, we introduce longEM-IQA and longEM-Bench, a training and evaluation suite comprising 11 in-phase/quadrature (I/Q)–language tasks spanning signal localization, identification, parameter estimation, and composite-entity understanding. To mitigate perceptual degradation, we propose EM-Lens, a framework that actively localizes target signal segments within complex observations and extracts their physical parameters via explicit signal-processing tools, while retaining end-to-end perception of individual segments. Furthermore, we devise a hybrid training strategy to reconcile the optimization conflict between active analysis and end-to-end perception. Extensive experiments demonstrate that EM-Lens achieves state-of-the-art performance on longEM-Bench, showing that active acquisition of relevant signal segments coupled with explicit measurements enables more reliable observation-level EM signal understanding.