Meta-Autoregressive Modeling: A Higher-Order Framework for Generating, Adapting, and Optimizing Autoregressive Models
Abstract
Autoregressive models constitute a fundamental family of probabilistic models for sequential prediction and generation. By modeling the conditional distribution of each element given preceding elements, they support applications in language modeling, code generation, audio synthesis, time-series forecasting, and other sequential tasks. Despite extensive advances in autoregressive architectures, training procedures, sampling strategies, and model adaptation, the selection and construction of suitable autoregressive models remain important design problems. This paper proposes Meta-Autoregressive Modeling (MAM), a higher-order framework in which a meta-level system learns how to select, configure, compose, adapt, and construct autoregressive models according to task requirements, data availability, computational budgets, and reliability constraints. Rather than restricting learning to the next-element prediction performed by an individual autoregressive model, the framework treats the model and its generation mechanism as objects of higher-order decision-making. It maintains task-dependent profiles of candidate models, estimates the outcomes of alternative model-management actions, and selects among model reuse, configuration changes, parameter generation, fine-tuning, architecture construction, and rejection of unsuitable candidates. A cost-aware mathematical formulation is introduced to balance predictive or generative utility against computational expense, adaptation cost, uncertainty, and risk. The paper further presents a model-management architecture, an algorithmic procedure, a bounded recursive extension, and an empirical evaluation protocol. The framework is intended to unify several established techniques under an explicit model-management objective rather than to claim that autoregressive model generation, neural architecture search, hypernetworks, or meta-learning are individually new. Its effectiveness and distinctiveness remain to be established through implementation and controlled experiments.
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BibTeX
@misc{zhang2026metaautoregressive,
title = {{Meta-Autoregressive Modeling: A Higher-Order Framework for Generating, Adapting, and Optimizing Autoregressive Models}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23529863},
url = {https://zenodo.org/records/23529863},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Meta-Autoregressive Modeling: A Higher-Order Framework for Generating, Adapting, and Optimizing Autoregressive Models
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23529863
UR - https://zenodo.org/records/23529863
AB - Autoregressive models constitute a fundamental family of probabilistic models for sequential prediction and generation. By modeling the conditional distribution of each element given preceding elements, they support applications in language modeling, code generation, audio synthesis, time-series forecasting, and other sequential tasks. Despite extensive advances in autoregressive architectures, training procedures, sampling strategies, and model adaptation, the selection and construction of suitable autoregressive models remain important design problems. This paper proposes Meta-Autoregressive Modeling (MAM), a higher-order framework in which a meta-level system learns how to select, configure, compose, adapt, and construct autoregressive models according to task requirements, data availability, computational budgets, and reliability constraints. Rather than restricting learning to the next-element prediction performed by an individual autoregressive model, the framework treats the model and its generation mechanism as objects of higher-order decision-making. It maintains task-dependent profiles of candidate models, estimates the outcomes of alternative model-management actions, and selects among model reuse, configuration changes, parameter generation, fine-tuning, architecture construction, and rejection of unsuitable candidates. A cost-aware mathematical formulation is introduced to balance predictive or generative utility against computational expense, adaptation cost, uncertainty, and risk. The paper further presents a model-management architecture, an algorithmic procedure, a bounded recursive extension, and an empirical evaluation protocol. The framework is intended to unify several established techniques under an explicit model-management objective rather than to claim that autoregressive model generation, neural architecture search, hypernetworks, or meta-learning are individually new. Its effectiveness and distinctiveness remain to be established through implementation and controlled experiments.
ER -