{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: IEEE Communications Letters, 2026",
  "arxiv": "2607.11972",
  "author_total": 2,
  "authors": [
    {
      "name": "Ziyang Meng"
    },
    {
      "name": "Lu Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e4h64pvg8w7h2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.11972",
  "fulltext_url": null,
  "id": "e4h64pvg8w7h2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.11972",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.NI"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Advanced Wireless Communication Technologies"
    ]
  ],
  "openalex": "W7168415807",
  "pdf_url": "https://arxiv.org/pdf/2607.11972",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e4h64pvg8w7h2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:17Z",
      "native_id": "2607.11972",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.11972"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "SkillComm: Skill-Driven Semantic Communication for Sequential Workflows via Incremental Token Transmission",
  "title_text": "SkillComm: Skill-Driven Semantic Communication for Sequential Workflows via Incremental Token Transmission",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:17Z",
  "url": "https://research.draconitic.com/paper/e4h64pvg8w7h2",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1109/lcomm.2026.3737163",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}