AI 中文总结
针对无线视觉智能中工作流的通信瓶颈问题，提出SkillComm框架，利用可重复使用技能状态进行工作流感知令牌优先级排序和内存辅助令牌网格重建，实验表明该框架能降低令牌传输成本并保持精度。
AI 中文摘要
随着无线视觉智能从孤立任务推理发展到有序技能工作流，通信瓶颈从传输单个语义表示转变为在信道约束下协调可重复使用的技能状态。现有的DeepJSCC和提示引导视觉发射器通常将每个任务视为独立的全令牌传输，跨语义工作流的执行内存重用有限。为此，我们提出了SkillComm，一个技能驱动的语义通信框架，它使用可重复使用的技能状态作为共享上下文，用于工作流感知令牌优先级排序和内存辅助令牌网格重建。实验表明SkillComm在高信噪比下将令牌传输成本降低了51.2%，同时保持了99.4%的上限归一化平均精度。这些结果表明，可重复使用的技能状态能够为未来的智能视觉和具身视觉智能提供选择性语义更新传递。
英文摘要
As wireless visual intelligence evolves from isolated task inference to ordered skill workflows, the communication bottleneck shifts from transmitting a single semantic representation to coordinating reusable skill states under channel constraints. Existing DeepJSCC and prompt-guided visual transmitters usually treat each task as an independent full-token transmission, with limited reuse of execution memory across semantic workflows. This is inefficient for workflows such as Detect, Segment, and Keypoint, where later stages often require only state-relevant semantic updates. To this end, we propose SkillComm, a skill-driven semantic communication framework that uses reusable skill states as shared context for workflow-aware token prioritization and memory-assisted token-grid reconstruction. A shared Skill-Book maps a high-level visual intent into a synchronized executable skill sequence at the transmitter and receiver. Conditioned on this workflow, adaptive token selection exploits cross-step memory to transmit only state-active tokens through joint source-channel coding, while the receiver reconstructs a task-ready token grid by combining decoded tokens with local historical memory. Experiments on the MS COCO 2017 validation set for the Detect-Segment-Keypoint workflow show that SkillComm reduces token transmission cost by 51.2% while retaining 99.4% upper-bound-normalized average precision at high SNR. These results demonstrate that reusable skill states enable selective semantic update delivery for future agentic and embodied visual intelligence.
Journal refIEEE Communications Letters, 2026
DOI：10.1109/LCOMM.2026.3737163
