Towards Task-Oriented Semantic Communication: A Survey on Technologies, Opportunities, and Challenges

The growing demand for intelligent and adaptive services is accelerating the transition from conventional communication systems to semantic communication. Task-Oriented Semantic Communication (ToSC) has emerged as an efficient framework that reduces communication cost by transmitting only task-relevant information for downstream processing. This survey reviews the theoretical foundations, system architectures, and enabling technologies of ToSC, providing a taxonomy of design strategies spanning collaborative edge inference, end-to-end optimization, and multi-agent cooperation. Key technologies, including deep learning-based semantic representation and adaptive joint source-channel coding, are discussed. Device heterogeneity and partial observability are examined alongside recent tasks' performance evaluations, such as classification and recognition. The survey also discusses the integration of large-scale multimodal foundation models into semantic encoders for zero-shot task adaptation, continual learning frameworks for on-device semantic processing, cooperative multi-agent reasoning with interpretable information exchange, and cross-layer co-design within AI-native 6G infrastructures, leveraging reconfigurable intelligent surfaces and digital twins. By consolidating existing knowledge and identifying key design trade-offs in latency, accuracy, and bandwidth efficiency, this work aims to serve as both a reference and a roadmap for advancing ToSC toward reliable and resource-efficient communication in 6G and beyond.