{
  "abstract": "Transformers have become a foundational architecture in natural language processing, computer vision, multimodal learning, time-series analysis, and numerous other machine-learning applications. Research has extensively investigated Transformer architectures, attention mechanisms, parameter-efficient adaptation, automated architecture search, and model compression. However, an important systems-level question remains: can a learning system explicitly learn how to construct, select, modify, combine, and optimize Transformer models themselves? This paper proposes Meta-Transformer Modeling (MTM), a higher-order framework that treats Transformer models as objects of adaptive construction and management. Given a task specification, available data, computational constraints, and a repository of existing models and components, an MTM system estimates the suitability of alternative designs and selects among model reuse, configuration, component composition, parameter adaptation, architecture generation, and model-level replacement. The framework represents each candidate Transformer through a structured profile of capabilities, computational costs, uncertainty, compatibility, and reliability. It then uses a meta-level policy to determine which intervention is expected to provide the greatest task-specific benefit under explicit constraints. The paper formalizes the model-management problem, presents a proposed architecture and algorithm, introduces a cost-aware objective, and describes a bounded recursive extension in which the meta-level mechanism itself can be evaluated and improved. It also provides testable hypotheses and an experimental protocol for comparison with conventional Transformer architecture search, hypernetwork-based parameter generation, fixed model selection, and parameter-efficient adaptation. The proposed framework is conceptual and has not been empirically validated. Its research contribution lies in formulating the coordinated management of Transformer construction and adaptation as a higher-order learning problem, while recognizing that its individual mechanisms overlap with established research and that any distinctive contribution must be established experimentally.",
  "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/49zb65pxf2f84.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23529837",
  "fulltext_url": null,
  "id": "49zb65pxf2f84",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23529837",
  "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/23529837/files/Meta-Transformer%20Modeling.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/49zb65pxf2f84.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:21:16Z",
      "native_id": "23529837",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23529837"
    }
  ],
  "subjects": [],
  "title": "Meta-Transformer Modeling: A Higher-Order Framework for Generating, Adapting, and Optimizing Transformer Models",
  "title_text": "Meta-Transformer Modeling: A Higher-Order Framework for Generating, Adapting, and Optimizing Transformer Models",
  "type": "preprint",
  "updated_at": "2026-11-04T12:21:16Z",
  "url": "https://research.draconitic.com/paper/49zb65pxf2f84",
  "venue": "Zenodo",
  "versions": []
}