SenseAgent: Signal-Grounded Wireless Sensing via a Training-Free Mixture of Experts

Abstract

Large-language-model (LLM) agents for wireless systems have emerged rapidly, but nearly all of them live at the text or network-management layer. Knowledge-layer models answer questions about signals, orchestration-layer agents route intents over text and KPI metadata, and image-layer prompting operates on pre-computed CSI heatmaps; none of these consume or produce raw physical-layer RF signals. We introduce SenseAgent, a signal-grounded wireless sensing agent organized as a training-free mixture of experts (MoE-TF-DM), in which each expert is a training-free diffusion module specialized for one RF modality (RFID activity, WiFi CSI, or IQ modulation) and an LLM serves as both the modality gate and the natural-language interpreter over 11 signal-processing tools. Unlike classical mixture-of-experts baselines for wireless, whose priors live in pretrained expert weights and require retraining for each new class or environment, SenseAgent’s experts bind the prior to reference samples and adapt at inference time from a handful of few-shot exemplars. We support this architecture with a tool-selection evaluation harness of 30 natural-language queries that exercises top-1 accuracy, description robustness, and an LLM-only ablation. The agent reaches 96.7% top-1 tool selection at verbose default, drops to 23.3% when tool descriptions are truncated to a single sentence, and falls to zero when the LLM is asked to produce the same outputs without any tools, confirming that the tool layer is constitutive rather than decorative. On RadioML 2016.10a, the training-free IQ expert improves over a source-only baseline on 28 of 30 benchmark cells spanning a range of few-shot budgets and signal-to-noise ratios, with a mean accuracy gain of 6.8 percentage points, while running at roughly 1/180-th the synthesis time of a matched trained DDPM, directly quantifying the advantage of a data-bound prior under few-shot target-domain conditions. Lightweight case studies across IQ modulation, RFID, and WiFi CSI exercise zero-retraining adaptation through the full ReAct loop. Full quantitative per-modality studies are the subject of ongoing journal-length work; this invited paper establishes the agent framework, its evaluation, and a research agenda for signal-grounded sensing agents in 6G AI-native networks.