Agent-Native Task-Oriented Communication with Joint Token Compression Coding and Modulation

Abstract

As large foundation models empower agents to become pervasive across industries and emerge as central actors in intelligent systems, a fundamental rethinking of communication paradigms toward AI-native, agent-centric designs in the post-Shannon era becomes inevitable. One essential shift is that tokens, which are the minimal semantic units natively processed by large language models (LLMs), should replace bits as the fundamental unit of communication. However, existing works in the LLMs field assume lossless token transmission over high-speed wired links and largely neglect the air-interface overhead and channel distortions inherent in wireless environments, lacking a native design for wireless token communication systems. To bridge this gap, we propose an innovative design for a token transmitter-receiver architecture that facilitates task-oriented token transmission. Specifically, we propose JTCM (Joint Token Coding and Modulation), an AI-native semantic communication framework that jointly optimizes token representation, channel coding, and modulation to maximize downstream task performance directly. Correspondingly, we propose a two-stage training scheme. In the first stage, the token encoder-decoder pair is pre-trained to enable semantic-preserving compression and reconstruction. In the second stage, it is fine-tuned end-to-end with a multi-modal foundation model under specific downstream tasks to achieve task-aware optimization. Extensive experiments demonstrate that JTCM significantly reduces transmission overhead while enhancing task accuracy and robustness compared to state-of-the-art baselines in bandwidth- and SNR-constrained wireless channels.