The Self-and-Mutual-and-Meta Model: A General Computational Framework for Adaptive Coordination, Selection, and Regulation of Autonomous and Interactive Mechanisms
Abstract
Computational systems increasingly rely on both autonomous processing within individual components and interactions among multiple components. Autonomous mechanisms can support self-reflection, self-diagnosis, self-correction, self-verification, and individual planning, whereas interactive mechanisms can facilitate mutual evaluation, collaborative planning, cross-component verification, and distributed problem-solving. However, selecting between autonomous and interactive processing introduces a further computational problem: the system must determine which mechanisms to activate, when to activate them, how to configure their interactions, and when their continued execution is no longer beneficial. This paper proposes the Self-and-Mutual-and-Meta Model (SMM), a conceptual computational framework that integrates three functional levels: self-mechanisms, mutual mechanisms, and meta-mechanisms. Self-mechanisms perform autonomous processing; mutual mechanisms coordinate interactions among multiple computational entities; and meta-mechanisms monitor, select, configure, evaluate, and regulate both autonomous and interactive processing. Unlike a framework that merely switches between individual-model and multi-model execution, SMM explicitly represents the control of the mechanisms responsible for that switching. The framework formalizes this relationship as a hierarchical sequential decision problem in which a meta-controller selects processing mechanisms and interaction configurations according to task state, uncertainty, historical outcomes, evidence quality, computational resources, and operational constraints. A reference architecture, formal objective, adaptive control algorithm, mechanism taxonomy, research hypotheses, and experimental protocol are presented. The framework also distinguishes inference-time meta-control from parameter-level learning and examines risks arising from correlated errors, inaccurate meta-evaluation, unstable switching, and excessive control complexity. SMM is proposed as a research framework rather than an empirically validated system. Its potential contribution is a unified formulation of autonomous processing, interactive processing, and the meta-level regulation of both, whose methodological value must be established through comparison with existing hierarchical control, metacognitive, routing, and adaptive multi-agent approaches.
Related
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BibTeX
@misc{zhang2026selfandmutualandmeta,
title = {{The Self-and-Mutual-and-Meta Model: A General Computational Framework for Adaptive Coordination, Selection, and Regulation of Autonomous and Interactive Mechanisms}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23434019},
url = {https://zenodo.org/records/23434019},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Mutual-and-Meta Model: A General Computational Framework for Adaptive Coordination, Selection, and Regulation of Autonomous and Interactive Mechanisms
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23434019
UR - https://zenodo.org/records/23434019
AB - Computational systems increasingly rely on both autonomous processing within individual components and interactions among multiple components. Autonomous mechanisms can support self-reflection, self-diagnosis, self-correction, self-verification, and individual planning, whereas interactive mechanisms can facilitate mutual evaluation, collaborative planning, cross-component verification, and distributed problem-solving. However, selecting between autonomous and interactive processing introduces a further computational problem: the system must determine which mechanisms to activate, when to activate them, how to configure their interactions, and when their continued execution is no longer beneficial. This paper proposes the Self-and-Mutual-and-Meta Model (SMM), a conceptual computational framework that integrates three functional levels: self-mechanisms, mutual mechanisms, and meta-mechanisms. Self-mechanisms perform autonomous processing; mutual mechanisms coordinate interactions among multiple computational entities; and meta-mechanisms monitor, select, configure, evaluate, and regulate both autonomous and interactive processing. Unlike a framework that merely switches between individual-model and multi-model execution, SMM explicitly represents the control of the mechanisms responsible for that switching. The framework formalizes this relationship as a hierarchical sequential decision problem in which a meta-controller selects processing mechanisms and interaction configurations according to task state, uncertainty, historical outcomes, evidence quality, computational resources, and operational constraints. A reference architecture, formal objective, adaptive control algorithm, mechanism taxonomy, research hypotheses, and experimental protocol are presented. The framework also distinguishes inference-time meta-control from parameter-level learning and examines risks arising from correlated errors, inaccurate meta-evaluation, unstable switching, and excessive control complexity. SMM is proposed as a research framework rather than an empirically validated system. Its potential contribution is a unified formulation of autonomous processing, interactive processing, and the meta-level regulation of both, whose methodological value must be established through comparison with existing hierarchical control, metacognitive, routing, and adaptive multi-agent approaches.
ER -