Meta-State-Space Models: A General Framework for Generating, Adapting, and Restructuring State-Space Models Through a Higher-Level Dynamic Mechanism
Abstract
State-space models (SSMs) provide a general framework for representing latent states, temporal transitions, and observations in dynamical systems. However, conventional state-space modeling typically assumes that the model structure, state representation, transition mechanism, and observation mechanism are predefined or adapted within a specified model family. This paper proposes Meta-State-Space Models (Meta-SSMs) as a conceptual framework in which a higher-level mechanism dynamically constructs, selects, configures, and restructures state-space models according to observed data, task requirements, environmental conditions, uncertainty, and computational constraints. Rather than merely estimating the latent state of a system, the meta-mechanism operates on the mechanisms that define how latent states are represented, updated, and translated into observations. We formalize the framework using a meta-state that summarizes the current configuration of the underlying state-space model, a candidate-model generator, a model-selection mechanism, and an adaptive update rule. The framework distinguishes parameter adaptation, mechanism adaptation, structural adaptation, and model-family generation. We introduce a multi-objective formulation balancing predictive performance, uncertainty, computational cost, adaptation cost, and structural complexity. We further discuss hierarchical state-space modeling, recursive meta-adaptation, stability, identifiability, and evaluation protocols. Importantly, existing research on meta-learning for neural state-space models and meta-state-space representations of stochastic dynamical systems establishes substantial conceptual overlap with parts of this proposal. Accordingly, the contribution claimed here is a proposed unifying formulation rather than a demonstrated first invention of meta-state-space modeling. No empirical superiority is claimed; experiments are required to determine whether the proposed framework provides measurable benefits over existing approaches.
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BibTeX
@misc{zhang2026metastatespace,
title = {{Meta-State-Space Models: A General Framework for Generating, Adapting, and Restructuring State-Space Models Through a Higher-Level Dynamic Mechanism}},
author = {Jinchen Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23530028},
url = {https://zenodo.org/records/23530028},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Meta-State-Space Models: A General Framework for Generating, Adapting, and Restructuring State-Space Models Through a Higher-Level Dynamic Mechanism
AU - Jinchen Zhang
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23530028
UR - https://zenodo.org/records/23530028
AB - State-space models (SSMs) provide a general framework for representing latent states, temporal transitions, and observations in dynamical systems. However, conventional state-space modeling typically assumes that the model structure, state representation, transition mechanism, and observation mechanism are predefined or adapted within a specified model family. This paper proposes Meta-State-Space Models (Meta-SSMs) as a conceptual framework in which a higher-level mechanism dynamically constructs, selects, configures, and restructures state-space models according to observed data, task requirements, environmental conditions, uncertainty, and computational constraints. Rather than merely estimating the latent state of a system, the meta-mechanism operates on the mechanisms that define how latent states are represented, updated, and translated into observations. We formalize the framework using a meta-state that summarizes the current configuration of the underlying state-space model, a candidate-model generator, a model-selection mechanism, and an adaptive update rule. The framework distinguishes parameter adaptation, mechanism adaptation, structural adaptation, and model-family generation. We introduce a multi-objective formulation balancing predictive performance, uncertainty, computational cost, adaptation cost, and structural complexity. We further discuss hierarchical state-space modeling, recursive meta-adaptation, stability, identifiability, and evaluation protocols. Importantly, existing research on meta-learning for neural state-space models and meta-state-space representations of stochastic dynamical systems establishes substantial conceptual overlap with parts of this proposal. Accordingly, the contribution claimed here is a proposed unifying formulation rather than a demonstrated first invention of meta-state-space modeling. No empirical superiority is claimed; experiments are required to determine whether the proposed framework provides measurable benefits over existing approaches.
ER -