Modern signal processing (SP) pipelines, whether model-based or data-driven, often constrained by complex and fragmented workflow, rely heavily on expert knowledge and manual engineering, and struggle with adaptability and generalization under limited data. In contrast, Large Language Models (LLMs) offer strong reasoning capabilities, broad general-purpose knowledge, in-context learning, and cross-modal transfer abilities, positioning them as powerful tools for automating and generalizing SP workflows. Motivated by these potentials, we introduce SignalLLM, the first general-purpose LLM-based agent framework for general SP tasks. Unlike prior LLM-based SP approaches that are limited to narrow applications or tricky prompting, SignalLLM introduces a principled, modular architecture. It decomposes high-level SP goals into structured subtasks via in-context learning and domain-specific retrieval, followed by hierarchical planning through adaptive retrieval-augmented generation (RAG) and refinement; these subtasks are then executed through prompt-based reasoning, cross-modal reasoning, code synthesis, model invocation, or data-driven LLM-assisted modeling. Its generalizable design enables the flexible selection of problem solving strategies across different signal modalities, task types, and data conditions. We demonstrate the versatility and effectiveness of SignalLLM through five representative tasks in communication and sensing, such as radar target detection, human activity recognition, and text compression. Experimental results show superior performance over traditional and existing LLM-based methods, particularly in few-shot and zero-shot settings.

翻译：现代信号处理（SP）流程，无论是基于模型还是数据驱动，常受限于复杂且碎片化的工作流，严重依赖专家知识与人工工程，并在数据有限的情况下难以实现适应性与泛化性。相比之下，大型语言模型（LLMs）具备强大的推理能力、广泛的通用知识、上下文学习以及跨模态迁移能力，使其成为自动化与泛化SP工作流的有力工具。基于这些潜力，我们提出了SignalLLM，首个面向通用SP任务的基于LLM的通用智能体框架。与先前局限于狭窄应用或复杂提示的基于LLM的SP方法不同，SignalLLM引入了一种原则化、模块化的架构。它通过上下文学习和领域特定检索将高层SP目标分解为结构化子任务，随后通过自适应检索增强生成（RAG）与精炼进行分层规划；这些子任务随后通过基于提示的推理、跨模态推理、代码合成、模型调用或数据驱动的LLM辅助建模来执行。其可泛化的设计使得能够灵活选择跨不同信号模态、任务类型和数据条件的问题解决策略。我们通过在通信与感知领域的五个代表性任务（如雷达目标检测、人体活动识别和文本压缩）中展示了SignalLLM的多功能性与有效性。实验结果表明，其性能优于传统方法及现有基于LLM的方法，尤其在少样本和零样本设置下表现突出。