The Self-and-Mutual-and-Information Model: A General Computational Framework for Adaptive Coordination Between Autonomous Processing, Interactive Mechanisms, and Information Dynamics
Abstract
Computational systems increasingly operate in environments where effective problem solving depends not only on the selection of internal processing mechanisms or interactions among multiple components, but also on how information is generated, represented, acquired, transmitted, evaluated, integrated, and updated. Existing computational architectures provide numerous approaches to autonomous processing, collaborative interaction, information retrieval, knowledge integration, and communication management. However, these operations are frequently designed as separate components or organized through predetermined workflows. This separation creates a fundamental design problem: a system must determine not only whether to process a task independently or collaboratively, but also which information to obtain, exchange, preserve, verify, transform, or discard at each stage of computation.
This paper proposes the Self-and-Mutual-and-Information Model (SAM-I), a general conceptual framework for adaptive coordination among autonomous computational mechanisms, interactive mechanisms, and information dynamics. The framework distinguishes three complementary dimensions: self-mechanisms, which organize processing within an individual computational entity; mutual mechanisms, which organize interactions among multiple entities; and information mechanisms, which govern the availability, structure, quality, movement, transformation, and use of information. These dimensions are jointly coordinated through a state-aware controller that estimates task requirements, information uncertainty, evidence quality, information novelty, inter-model disagreement, communication cost, computational resources, and expected task improvement.
The proposed framework formulates adaptive coordination as a sequential decision problem in which processing organization and information operations are selected jointly. It introduces a multidimensional state representation, a cost-sensitive utility function, an adaptive control procedure, a mechanism library, and an experimental protocol for evaluating the framework against fixed processing strategies, conventional information-management pipelines, and adaptive collaboration methods. Potential applications include large language models, multi-agent systems, scientific computing, information retrieval, knowledge-intensive decision support, and adaptive computational architectures. The framework is a research proposal rather than an empirically validated system. Its central research proposition is that jointly adapting processing organization and information operations may provide advantages over systems that adapt either collaboration or information handling in isolation. The magnitude, generality, and originality of these advantages require systematic literature comparison and controlled empirical evaluation.
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BibTeX
@misc{zhang2026selfandmutualandinformation,
title = {{The Self-and-Mutual-and-Information Model: A General Computational Framework for Adaptive Coordination Between Autonomous Processing, Interactive Mechanisms, and Information Dynamics}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23434731},
url = {https://zenodo.org/records/23434731},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Mutual-and-Information Model: A General Computational Framework for Adaptive Coordination Between Autonomous Processing, Interactive Mechanisms, and Information Dynamics
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23434731
UR - https://zenodo.org/records/23434731
AB - Computational systems increasingly operate in environments where effective problem solving depends not only on the selection of internal processing mechanisms or interactions among multiple components, but also on how information is generated, represented, acquired, transmitted, evaluated, integrated, and updated. Existing computational architectures provide numerous approaches to autonomous processing, collaborative interaction, information retrieval, knowledge integration, and communication management. However, these operations are frequently designed as separate components or organized through predetermined workflows. This separation creates a fundamental design problem: a system must determine not only whether to process a task independently or collaboratively, but also which information to obtain, exchange, preserve, verify, transform, or discard at each stage of computation. This paper proposes the Self-and-Mutual-and-Information Model (SAM-I), a general conceptual framework for adaptive coordination among autonomous computational mechanisms, interactive mechanisms, and information dynamics. The framework distinguishes three complementary dimensions: self-mechanisms, which organize processing within an individual computational entity; mutual mechanisms, which organize interactions among multiple entities; and information mechanisms, which govern the availability, structure, quality, movement, transformation, and use of information. These dimensions are jointly coordinated through a state-aware controller that estimates task requirements, information uncertainty, evidence quality, information novelty, inter-model disagreement, communication cost, computational resources, and expected task improvement. The proposed framework formulates adaptive coordination as a sequential decision problem in which processing organization and information operations are selected jointly. It introduces a multidimensional state representation, a cost-sensitive utility function, an adaptive control procedure, a mechanism library, and an experimental protocol for evaluating the framework against fixed processing strategies, conventional information-management pipelines, and adaptive collaboration methods. Potential applications include large language models, multi-agent systems, scientific computing, information retrieval, knowledge-intensive decision support, and adaptive computational architectures. The framework is a research proposal rather than an empirically validated system. Its central research proposition is that jointly adapting processing organization and information operations may provide advantages over systems that adapt either collaboration or information handling in isolation. The magnitude, generality, and originality of these advantages require systematic literature comparison and controlled empirical evaluation.
ER -