SkillComm: Skill-Driven Semantic Communication for Sequential Workflows via Incremental Token Transmission
Abstract
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.
Versions
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BibTeX
@misc{meng2026skillcomm,
title = {{SkillComm: Skill-Driven Semantic Communication for Sequential Workflows via Incremental Token Transmission}},
author = {Ziyang Meng and Lu Lu},
howpublished = {arXiv},
year = {2026},
month = jul,
doi = {10.48550/arxiv.2607.11972},
eprint = {2607.11972},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.11972},
}
RIS
TY - UNPB
TI - SkillComm: Skill-Driven Semantic Communication for Sequential Workflows via Incremental Token Transmission
AU - Ziyang Meng
AU - Lu Lu
PY - 2026
DA - 2026/07/13
PB - arXiv
DO - 10.48550/arxiv.2607.11972
UR - https://arxiv.org/abs/2607.11972
AB - 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.
ER -