SkillComm: Skill-Driven Semantic Communication for Sequential Workflows via Incremental Token Transmission

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.