[2603.28183] PReD: An LLM-based Foundation Multimodal Model for Electromagnetic Perception, Recognition, and Decision

Computer Science > Artificial Intelligence arXiv:2603.28183 (cs) [Submitted on 30 Mar 2026] Title:PReD: An LLM-based Foundation Multimodal Model for Electromagnetic Perception, Recognition, and Decision Authors:Zehua Han, Jing Xiao, Yiqi Duan, Mengyu Xiang, Yuheng Ji, Xiaolong Zheng, Chenghanyu Zhang, Zhendong She, Junyu Shen, Dingwei Tan, Shichu Sun, Zhou Cong, Mingxuan Liu, Fengxiang Wang, Jinping Sun, Yangang Sun View a PDF of the paper titled PReD: An LLM-based Foundation Multimodal Model for Electromagnetic Perception, Recognition, and Decision, by Zehua Han and 15 other authors View PDF HTML (experimental) Abstract:Multimodal Large Language Models have demonstrated powerful cross-modal understanding and reasoning capabilities in general domains. However, in the electromagnetic (EM) domain, they still face challenges such as data scarcity and insufficient integration of domain knowledge. This paper proposes PReD, the first foundation model for the EM domain that covers the intelligent closed-loop of "perception, recognition, decision-making." We constructed a high-quality multitask EM dataset, PReD-1.3M, and an evaluation benchmark, PReD-Bench. The dataset encompasses multi-perspective representations such as raw time-domain waveform, frequency-domain spectrograms, and constellation diagrams, covering typical features of communication and radar signals. It supports a range of core tasks, including signal detection, modulation recognition, parameter estimation, protoco...