Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization
Read the original on arXiv AI →The paper introduces BATok, a budget‑adaptive tokenizer that converts raw I/Q electromagnetic signals into compact tokens suitable for multimodal large language models. BATok adjusts token capacity based on input length and signal content, using multi‑resolution features and a local energy prior to resample representations. The authors also present EMSpec‑Instruct, a dataset linking raw signals, waterfall images, and language instructions for tasks such as modulation recognition, structured detection, and signal grounding, and demonstrate that BATok achieves competitive performance across these tasks.
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