Beyond Individual Ownership: ZAC, a General Framework for Collective Provisioning of Resources and Capabilities
Abstract
Many engineered and intelligent systems are designed around an implicit assumption: each individual unit must independently possess the resources and capabilities required to perform its intended functions. This assumption can lead to duplicated infrastructure, underutilized assets, and unnecessary hardware or operational costs. Advances in networking, distributed systems, automation, and artificial intelligence make it possible to reconsider this design principle.
This paper proposes Zero-Agent-Compute (ZAC) as an initial framework for investigating an alternative approach: collective provisioning. Under this approach, selected individuals do not independently provision every resource or capability required by their tasks. Instead, they obtain eligible resources, functions, or services from other individuals or shared infrastructure within a coordinated system.
Although the initial motivation for ZAC arises from embodied agents that obtain artificial intelligence computation from other agents, the proposed framework is not restricted to computation. Its potential scope includes sensing, memory, knowledge, energy, physical equipment, specialized capabilities, and other resources or services that can be provided across system boundaries. The framework distinguishes local provisioning, collective provisioning, and hybrid provisioning, and models their relationships through demand, capacity, allocation, service quality, coordination cost, and failure risk.
A general optimization formulation is introduced to examine the conditions under which collective provisioning may reduce total system cost while satisfying task, availability, reliability, and operational constraints. The paper also identifies resource-specific requirements, discusses failure modes, and proposes an evaluation methodology spanning heterogeneous resource classes.
ZAC is presented as a research framework and a set of testable design hypotheses, rather than as an experimentally established paradigm. Its novelty relative to existing work in distributed systems, cloud computing, service composition, resource pooling, and collaborative robotics remains to be established through systematic literature analysis. The central research question is whether a common provisioning model can explain and improve systems in which resource ownership is replaced, in part, by coordinated access to collective capabilities.
Related
- Beyond Individual Large Language Models: A General Framework for Intelligent and Autonomous Provisioning of AI Capabilities
- Zero-Local-Provisioning for Training (ZLP-Training): A General Framework for Collective Provisioning of Training Capabilities
- Zero-Local-Provisioning (ZLP): A General Framework for Collective Provisioning of Resources, Capabilities, and Attributes
- ZAC-Perception: Collective Provisioning of Perceptual Capabilities in Multi-Agent Embodied Intelligence
- Beyond GPT Ownership: Intelligent and Autonomous Possession as a General Framework for Need-Driven Access to Resources, Capabilities, and Services
BibTeX
@misc{zhang2026beyond,
title = {{Beyond Individual Ownership: ZAC, a General Framework for Collective Provisioning of Resources and Capabilities}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23449053},
url = {https://zenodo.org/records/23449053},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Individual Ownership: ZAC, a General Framework for Collective Provisioning of Resources and Capabilities
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23449053
UR - https://zenodo.org/records/23449053
AB - Many engineered and intelligent systems are designed around an implicit assumption: each individual unit must independently possess the resources and capabilities required to perform its intended functions. This assumption can lead to duplicated infrastructure, underutilized assets, and unnecessary hardware or operational costs. Advances in networking, distributed systems, automation, and artificial intelligence make it possible to reconsider this design principle. This paper proposes Zero-Agent-Compute (ZAC) as an initial framework for investigating an alternative approach: collective provisioning. Under this approach, selected individuals do not independently provision every resource or capability required by their tasks. Instead, they obtain eligible resources, functions, or services from other individuals or shared infrastructure within a coordinated system. Although the initial motivation for ZAC arises from embodied agents that obtain artificial intelligence computation from other agents, the proposed framework is not restricted to computation. Its potential scope includes sensing, memory, knowledge, energy, physical equipment, specialized capabilities, and other resources or services that can be provided across system boundaries. The framework distinguishes local provisioning, collective provisioning, and hybrid provisioning, and models their relationships through demand, capacity, allocation, service quality, coordination cost, and failure risk. A general optimization formulation is introduced to examine the conditions under which collective provisioning may reduce total system cost while satisfying task, availability, reliability, and operational constraints. The paper also identifies resource-specific requirements, discusses failure modes, and proposes an evaluation methodology spanning heterogeneous resource classes. ZAC is presented as a research framework and a set of testable design hypotheses, rather than as an experimentally established paradigm. Its novelty relative to existing work in distributed systems, cloud computing, service composition, resource pooling, and collaborative robotics remains to be established through systematic literature analysis. The central research question is whether a common provisioning model can explain and improve systems in which resource ownership is replaced, in part, by coordinated access to collective capabilities.
ER -