The Self-and-Mutual Structure Model: A General Computational Framework for Adaptive Structural Organization and Reorganization in Multiple Large Language Models
Abstract
Large language models (LLMs) and other computational systems depend not only on the quality of their individual processing mechanisms but also on the structures through which computational operations, information, tasks, and decision-making responsibilities are organized. Within a single model or computational agent, internal task decomposition, processing stages, dependency relationships, and module arrangements determine how individual operations are coordinated. Across multiple models or agents, centralized, decentralized, hierarchical, and parallel structures determine how information is exchanged, responsibilities are distributed, and intermediate results are integrated. However, a fixed structure may be inefficient when task requirements change, while frequent structural reorganization may introduce additional computation, communication overhead, coordination instability, and unnecessary complexity.
This paper proposes the Self-and-Mutual Structure Model (SMSM), a conceptual computational framework for adaptively organizing and reorganizing internal computational structures and inter-agent coordination structures. The framework distinguishes self-structural mechanisms, which organize operations within an individual computational entity, from mutual-structural mechanisms, which organize relationships among multiple entities. A structure-aware controller evaluates task requirements, dependency patterns, uncertainty, structural bottlenecks, communication costs, resource constraints, and historical structural performance to select, retain, modify, or replace an appropriate organization. The framework supports transitions among alternative internal structures, communication topologies, task-allocation patterns, and hybrid configurations. It formulates structural adaptation as a constrained sequential decision problem and introduces a cost-sensitive objective balancing task quality, reliability, computational expenditure, latency, and structural reorganization costs. A reference architecture, formal problem definition, adaptive algorithm, research hypotheses, and experimental protocol are presented. The framework is a research proposal rather than an empirically validated system. Because adaptive multi-agent communication topology and structural reorganization have already been investigated in prior work, the principal research question is whether a unified controller that explicitly connects individual computational organization with inter-agent organization can provide measurable advantages over existing structural adaptation methods.
Related
- Integrative structural interactomics reveals protein organization and structure in a giant virus
- Structural, supramolecular and computational insights into the ALS inhibitor pyroxsulam: crystal structure, reactivity and bioactivity
- Hemodynamic-Structural Multi-Fidelity Assessment of Endovascular Flow Diversion Strategies for Cerebral Aneurysm Treatment: An Integrated Computational and Experimental Framework
- DeCoRE: A computational method to resolve RNA structural heterogeneity from RNA structure probing data by direct RNA sequencing
- Computational framework NetBiF predicts and classifies steady-state bifurcations in biochemical systems via reaction network structure
BibTeX
@misc{zhang2026selfandmutual,
title = {{The Self-and-Mutual Structure Model: A General Computational Framework for Adaptive Structural Organization and Reorganization in Multiple Large Language Models}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23434755},
url = {https://zenodo.org/records/23434755},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Mutual Structure Model: A General Computational Framework for Adaptive Structural Organization and Reorganization in Multiple Large Language Models
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23434755
UR - https://zenodo.org/records/23434755
AB - Large language models (LLMs) and other computational systems depend not only on the quality of their individual processing mechanisms but also on the structures through which computational operations, information, tasks, and decision-making responsibilities are organized. Within a single model or computational agent, internal task decomposition, processing stages, dependency relationships, and module arrangements determine how individual operations are coordinated. Across multiple models or agents, centralized, decentralized, hierarchical, and parallel structures determine how information is exchanged, responsibilities are distributed, and intermediate results are integrated. However, a fixed structure may be inefficient when task requirements change, while frequent structural reorganization may introduce additional computation, communication overhead, coordination instability, and unnecessary complexity. This paper proposes the Self-and-Mutual Structure Model (SMSM), a conceptual computational framework for adaptively organizing and reorganizing internal computational structures and inter-agent coordination structures. The framework distinguishes self-structural mechanisms, which organize operations within an individual computational entity, from mutual-structural mechanisms, which organize relationships among multiple entities. A structure-aware controller evaluates task requirements, dependency patterns, uncertainty, structural bottlenecks, communication costs, resource constraints, and historical structural performance to select, retain, modify, or replace an appropriate organization. The framework supports transitions among alternative internal structures, communication topologies, task-allocation patterns, and hybrid configurations. It formulates structural adaptation as a constrained sequential decision problem and introduces a cost-sensitive objective balancing task quality, reliability, computational expenditure, latency, and structural reorganization costs. A reference architecture, formal problem definition, adaptive algorithm, research hypotheses, and experimental protocol are presented. The framework is a research proposal rather than an empirically validated system. Because adaptive multi-agent communication topology and structural reorganization have already been investigated in prior work, the principal research question is whether a unified controller that explicitly connects individual computational organization with inter-agent organization can provide measurable advantages over existing structural adaptation methods.
ER -