The Self-and-Mutual Resources Model: A Unified Framework for Adaptive Resource Management and Cross-System Resource Coordination in Multiple Large Language Models
Abstract
Large language models (LLMs) operate under resource constraints involving computational capacity, inference budgets, memory, context windows, information access, tool availability, communication bandwidth, latency, and energy consumption. Although an individual model can manage its own resource allocation, complex tasks may benefit from sharing, pooling, or coordinating resources across multiple models and agents. However, neither isolated resource management nor unrestricted resource sharing is necessarily optimal. Independent management may leave complementary resources underutilized, whereas excessive sharing and coordination may introduce communication overhead, resource contention, duplicated computation, and additional failure dependencies.
This paper proposes the Self-and-Mutual Resources Model (SMRM), a conceptual framework for adaptive switching between self-resource mechanisms and mutual-resource mechanisms in multi-LLM computational systems. Self-resource mechanisms manage resources within an individual model or agent, whereas mutual-resource mechanisms coordinate resource acquisition, allocation, sharing, exchange, and utilization across multiple agents. The framework treats resource management as a sequential decision problem in which a controller selects an appropriate mechanism according to task requirements, resource availability, expected marginal utility, resource scarcity, coordination costs, uncertainty, and remaining budgets. It supports individual resource optimization, collaborative allocation, selective resource sharing, cross-agent resource reassignment, and transitions between resource-management strategies. A resource-sensitive objective is formulated to balance task quality, reliability, resource expenditure, latency, and coordination overhead, subject to hard operational constraints. The paper defines the conceptual model, develops a formal decision framework, presents an adaptive resource-management algorithm, identifies failure modes, and proposes an empirical evaluation protocol. SMRM is a research proposal rather than an empirically validated system. Its potential contribution is a unified abstraction for studying when resources should remain locally managed and when they should be coordinated across agents, subject to comparison with existing resource allocation, scheduling, routing, and multi-agent coordination methods.
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BibTeX
@misc{zhang2026selfandmutual,
title = {{The Self-and-Mutual Resources Model: A Unified Framework for Adaptive Resource Management and Cross-System Resource Coordination in Multiple Large Language Models}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23434778},
url = {https://zenodo.org/records/23434778},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Mutual Resources Model: A Unified Framework for Adaptive Resource Management and Cross-System Resource Coordination in Multiple Large Language Models
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23434778
UR - https://zenodo.org/records/23434778
AB - Large language models (LLMs) operate under resource constraints involving computational capacity, inference budgets, memory, context windows, information access, tool availability, communication bandwidth, latency, and energy consumption. Although an individual model can manage its own resource allocation, complex tasks may benefit from sharing, pooling, or coordinating resources across multiple models and agents. However, neither isolated resource management nor unrestricted resource sharing is necessarily optimal. Independent management may leave complementary resources underutilized, whereas excessive sharing and coordination may introduce communication overhead, resource contention, duplicated computation, and additional failure dependencies. This paper proposes the Self-and-Mutual Resources Model (SMRM), a conceptual framework for adaptive switching between self-resource mechanisms and mutual-resource mechanisms in multi-LLM computational systems. Self-resource mechanisms manage resources within an individual model or agent, whereas mutual-resource mechanisms coordinate resource acquisition, allocation, sharing, exchange, and utilization across multiple agents. The framework treats resource management as a sequential decision problem in which a controller selects an appropriate mechanism according to task requirements, resource availability, expected marginal utility, resource scarcity, coordination costs, uncertainty, and remaining budgets. It supports individual resource optimization, collaborative allocation, selective resource sharing, cross-agent resource reassignment, and transitions between resource-management strategies. A resource-sensitive objective is formulated to balance task quality, reliability, resource expenditure, latency, and coordination overhead, subject to hard operational constraints. The paper defines the conceptual model, develops a formal decision framework, presents an adaptive resource-management algorithm, identifies failure modes, and proposes an empirical evaluation protocol. SMRM is a research proposal rather than an empirically validated system. Its potential contribution is a unified abstraction for studying when resources should remain locally managed and when they should be coordinated across agents, subject to comparison with existing resource allocation, scheduling, routing, and multi-agent coordination methods.
ER -