{
  "abstract": "Image generation can be organized around different principles, including pixel placement, diffusion, growth, fluid motion, and spatial subdivision. This paper proposes Territory-Competition Image Generation (TCIG), a conceptual framework in which an image is constructed through the competition of spatially distributed entities for ownership of image regions. Unlike a conventional static Voronoi construction, the proposed framework treats region allocation as an evolving process in which competitors may change their positions, influence, appearance, and internal states. The framework incorporates weighted spatial competition, adaptive boundary formation, variable-population dynamics, reward-driven strategy updates, and quality–cost optimization. Each competitor generates or controls the visual content associated with its territory, while a global evaluation mechanism assesses the coherence and quality of the resulting image. Competitors may expand, contract, migrate, split, merge, appear, or disappear according to predefined rules or learned policies. The framework is designed to support a progression from procedural geometric patterns to structured visual synthesis and, potentially, text-conditioned image generation. This paper formalizes the framework, specifies its operational components, proposes learning and optimization objectives, and outlines an experimental protocol for evaluating its effectiveness. The approach is presented as a research proposal rather than an empirically validated image-generation system. Its potential contribution lies in integrating dynamic territorial competition, adaptive population management, and reward-driven visual synthesis within a unified generation process.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Jincheng Zhang",
      "orcid": "0009-0005-1860-0009"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7egfqw98w6dxq.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23556009",
  "fulltext_url": null,
  "id": "7egfqw98w6dxq",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23556009",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23556009/files/Territory-Competition%20Image%20Generation.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7egfqw98w6dxq.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:20:46Z",
      "native_id": "23556009",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23556009"
    }
  ],
  "subjects": [],
  "title": "Territory-Competition Image Generation: A Dynamic Framework for Adaptive Spatial Allocation, Region Evolution, and Reward-Driven Visual Synthesis",
  "title_text": "Territory-Competition Image Generation: A Dynamic Framework for Adaptive Spatial Allocation, Region Evolution, and Reward-Driven Visual Synthesis",
  "type": "preprint",
  "updated_at": "2026-11-05T12:20:46Z",
  "url": "https://research.draconitic.com/paper/7egfqw98w6dxq",
  "venue": "Zenodo",
  "versions": []
}