The Self-and-Mutual-and-Action Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Goal-Directed Action
Abstract
Computational systems increasingly require more than internal processing or interaction among multiple components. They must also determine which actions to perform, when to execute them, how to allocate resources, and how to respond to the consequences of execution. Individual processing mechanisms, including self-reflection, self-diagnosis, self-correction, and self-planning, support autonomous computation. Mutual mechanisms, including collaborative planning, cross-model verification, mutual diagnosis, and knowledge exchange, introduce additional perspectives and coordination capabilities. However, neither individual processing nor multi-component interaction necessarily produces an appropriate action. A system may reason correctly but fail to act, select an inappropriate operation, execute a plan under changed conditions, or continue an action after its expected benefit has disappeared.
This paper proposes the Self-and-Mutual-and-Action Model (SMA), a conceptual computational framework for adaptively coordinating individual processing mechanisms, interactive mechanisms, and goal-directed action. The model introduces action as an explicit computational dimension rather than treating it merely as an implicit consequence of reasoning or collaboration. Its architecture supports transitions among self-processing, mutual interaction, action selection, execution, outcome monitoring, and post-action adaptation. A state-aware controller evaluates task requirements, uncertainty, model disagreement, expected action utility, execution cost, reversibility, environmental conditions, and remaining resources when determining the next operation. The framework distinguishes action proposal, action selection, action authorization, action execution, and outcome evaluation, thereby separating the generation of a candidate operation from permission to perform it and from evidence that it succeeded. A formal problem definition, a cost-sensitive objective, an adaptive control algorithm, mechanism configurations, safety constraints, research hypotheses, and an experimental evaluation protocol are presented. The framework is a conceptual proposal and has not yet been empirically validated. Its proposed contribution is the explicit integration of adaptive individual processing, multi-component interaction, and outcome-sensitive action control into a common computational architecture. Its distinctiveness and practical value require comparison with existing work on planning, tool use, agentic systems, feedback control, and adaptive multi-agent collaboration.
Related
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BibTeX
@misc{zhang2026selfandmutualandaction,
title = {{The Self-and-Mutual-and-Action Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Goal-Directed Action}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23434722},
url = {https://zenodo.org/records/23434722},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Mutual-and-Action Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Goal-Directed Action
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23434722
UR - https://zenodo.org/records/23434722
AB - Computational systems increasingly require more than internal processing or interaction among multiple components. They must also determine which actions to perform, when to execute them, how to allocate resources, and how to respond to the consequences of execution. Individual processing mechanisms, including self-reflection, self-diagnosis, self-correction, and self-planning, support autonomous computation. Mutual mechanisms, including collaborative planning, cross-model verification, mutual diagnosis, and knowledge exchange, introduce additional perspectives and coordination capabilities. However, neither individual processing nor multi-component interaction necessarily produces an appropriate action. A system may reason correctly but fail to act, select an inappropriate operation, execute a plan under changed conditions, or continue an action after its expected benefit has disappeared. This paper proposes the Self-and-Mutual-and-Action Model (SMA), a conceptual computational framework for adaptively coordinating individual processing mechanisms, interactive mechanisms, and goal-directed action. The model introduces action as an explicit computational dimension rather than treating it merely as an implicit consequence of reasoning or collaboration. Its architecture supports transitions among self-processing, mutual interaction, action selection, execution, outcome monitoring, and post-action adaptation. A state-aware controller evaluates task requirements, uncertainty, model disagreement, expected action utility, execution cost, reversibility, environmental conditions, and remaining resources when determining the next operation. The framework distinguishes action proposal, action selection, action authorization, action execution, and outcome evaluation, thereby separating the generation of a candidate operation from permission to perform it and from evidence that it succeeded. A formal problem definition, a cost-sensitive objective, an adaptive control algorithm, mechanism configurations, safety constraints, research hypotheses, and an experimental evaluation protocol are presented. The framework is a conceptual proposal and has not yet been empirically validated. Its proposed contribution is the explicit integration of adaptive individual processing, multi-component interaction, and outcome-sensitive action control into a common computational architecture. Its distinctiveness and practical value require comparison with existing work on planning, tool use, agentic systems, feedback control, and adaptive multi-agent collaboration.
ER -