Beyond Individual Large Language Models: A General Framework for Intelligent and Autonomous Provisioning of AI Capabilities
Abstract
The development and deployment of large language models (LLMs) have largely been organized around identifiable models, including their selection, acquisition, deployment, subscription, and use. However, users may not fundamentally require any particular model. Instead, they may require the intelligent capabilities that models provide, such as reasoning, writing, programming, planning, information retrieval, and problem-solving. This distinction motivates a shift from model-centered access toward capability-centered provisioning. Under this perspective, a model is not necessarily the primary object that users seek to possess; it is one possible resource through which their required capabilities can be obtained. This paper proposes Intelligent Capability Provisioning (ICP), a conceptual framework for the intelligent and autonomous acquisition, allocation, maintenance, and adaptation of AI capabilities according to changing user requirements. The framework distinguishes requested capabilities from their underlying implementations and considers local, external, collective, and hybrid provisioning arrangements. It introduces a formal representation of capability demand, implementation resources, provisioning configurations, quality constraints, and lifecycle costs. It further identifies autonomous demand interpretation, capability decomposition, implementation selection, dynamic reconfiguration, continuity management, and user-centered ownership as central research challenges. Unlike approaches that focus exclusively on selecting or combining models for individual requests, the proposed framework places the sustained satisfaction of capability requirements at the center of system design. This paper establishes a conceptual foundation and a set of testable research propositions rather than claiming empirical validation or demonstrated superiority over existing systems.
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BibTeX
@misc{zhang2026beyond,
title = {{Beyond Individual Large Language Models: A General Framework for Intelligent and Autonomous Provisioning of AI Capabilities}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23591713},
url = {https://zenodo.org/records/23591713},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Individual Large Language Models: A General Framework for Intelligent and Autonomous Provisioning of AI Capabilities
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23591713
UR - https://zenodo.org/records/23591713
AB - The development and deployment of large language models (LLMs) have largely been organized around identifiable models, including their selection, acquisition, deployment, subscription, and use. However, users may not fundamentally require any particular model. Instead, they may require the intelligent capabilities that models provide, such as reasoning, writing, programming, planning, information retrieval, and problem-solving. This distinction motivates a shift from model-centered access toward capability-centered provisioning. Under this perspective, a model is not necessarily the primary object that users seek to possess; it is one possible resource through which their required capabilities can be obtained. This paper proposes Intelligent Capability Provisioning (ICP), a conceptual framework for the intelligent and autonomous acquisition, allocation, maintenance, and adaptation of AI capabilities according to changing user requirements. The framework distinguishes requested capabilities from their underlying implementations and considers local, external, collective, and hybrid provisioning arrangements. It introduces a formal representation of capability demand, implementation resources, provisioning configurations, quality constraints, and lifecycle costs. It further identifies autonomous demand interpretation, capability decomposition, implementation selection, dynamic reconfiguration, continuity management, and user-centered ownership as central research challenges. Unlike approaches that focus exclusively on selecting or combining models for individual requests, the proposed framework places the sustained satisfaction of capability requirements at the center of system design. This paper establishes a conceptual foundation and a set of testable research propositions rather than claiming empirical validation or demonstrated superiority over existing systems.
ER -