@inproceedings{luo-tao-2026-aagisphere,
title = "{AA}gi{S}phere-{CORE}: A Multi-Dimensional Co-Optimization Framework for Autonomous Prescriptive Maintenance in Smart Manufacturing",
author = "Luo, Pinghao and
Tao, Zhiguo",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.2/",
pages = "18--32",
ISBN = "979-8-89176-478-1",
abstract = "Intelligent workflows for smart manufacturing reliability and maintenance (RxM) are transitioning from static, sequential multi-agent systems toward autonomous, dynamic decision-making. However, a critical gap persists between agent coordination and the holistic co-optimization required across predictive performance, computational resources, and domain-specific business logic. To address this, we propose \textbf{AgiSphere-CORE}, a novel framework centered on a \textbf{Co-Optimization Core (COC)}. The COC maintains a unified \textit{Collaborative State Vector} and solves a constrained optimization problem to maximize the \textit{Collaborative Utility} $J_{co}$, dynamically orchestrating specialized agents for resources, models, and logic. This enables a paradigm shift from sequential to co-optimization-driven autonomy. Extensive evaluation on multiple manufacturing tasks, including classification, regression, and anomaly detection, demonstrates that AgiSphere-CORE consistently outperforms baselines, achieving superior predictive performance (e.g., 97.3{\%} accuracy, 0.902 F1-score), improved resource efficiency (15{\%} faster workflow time), and higher estimated domain cost savings ({\$}148.7K). Ablation studies confirm the critical role of the COC and the adaptive learning mechanism. A detailed case study further illustrates the framework{'}s transparent, utility-driven trade-offs. We conclude that AgiSphere-CORE provides a robust, holistic foundation for next-generation autonomous RxM systems in smart manufacturing."
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<abstract>Intelligent workflows for smart manufacturing reliability and maintenance (RxM) are transitioning from static, sequential multi-agent systems toward autonomous, dynamic decision-making. However, a critical gap persists between agent coordination and the holistic co-optimization required across predictive performance, computational resources, and domain-specific business logic. To address this, we propose AgiSphere-CORE, a novel framework centered on a Co-Optimization Core (COC). The COC maintains a unified Collaborative State Vector and solves a constrained optimization problem to maximize the Collaborative Utility J_co, dynamically orchestrating specialized agents for resources, models, and logic. This enables a paradigm shift from sequential to co-optimization-driven autonomy. Extensive evaluation on multiple manufacturing tasks, including classification, regression, and anomaly detection, demonstrates that AgiSphere-CORE consistently outperforms baselines, achieving superior predictive performance (e.g., 97.3% accuracy, 0.902 F1-score), improved resource efficiency (15% faster workflow time), and higher estimated domain cost savings ($148.7K). Ablation studies confirm the critical role of the COC and the adaptive learning mechanism. A detailed case study further illustrates the framework’s transparent, utility-driven trade-offs. We conclude that AgiSphere-CORE provides a robust, holistic foundation for next-generation autonomous RxM systems in smart manufacturing.</abstract>
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%0 Conference Proceedings
%T AAgiSphere-CORE: A Multi-Dimensional Co-Optimization Framework for Autonomous Prescriptive Maintenance in Smart Manufacturing
%A Luo, Pinghao
%A Tao, Zhiguo
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F luo-tao-2026-aagisphere
%X Intelligent workflows for smart manufacturing reliability and maintenance (RxM) are transitioning from static, sequential multi-agent systems toward autonomous, dynamic decision-making. However, a critical gap persists between agent coordination and the holistic co-optimization required across predictive performance, computational resources, and domain-specific business logic. To address this, we propose AgiSphere-CORE, a novel framework centered on a Co-Optimization Core (COC). The COC maintains a unified Collaborative State Vector and solves a constrained optimization problem to maximize the Collaborative Utility J_co, dynamically orchestrating specialized agents for resources, models, and logic. This enables a paradigm shift from sequential to co-optimization-driven autonomy. Extensive evaluation on multiple manufacturing tasks, including classification, regression, and anomaly detection, demonstrates that AgiSphere-CORE consistently outperforms baselines, achieving superior predictive performance (e.g., 97.3% accuracy, 0.902 F1-score), improved resource efficiency (15% faster workflow time), and higher estimated domain cost savings ($148.7K). Ablation studies confirm the critical role of the COC and the adaptive learning mechanism. A detailed case study further illustrates the framework’s transparent, utility-driven trade-offs. We conclude that AgiSphere-CORE provides a robust, holistic foundation for next-generation autonomous RxM systems in smart manufacturing.
%U https://aclanthology.org/2026.groundlm-1.2/
%P 18-32
Markdown (Informal)
[AAgiSphere-CORE: A Multi-Dimensional Co-Optimization Framework for Autonomous Prescriptive Maintenance in Smart Manufacturing](https://aclanthology.org/2026.groundlm-1.2/) (Luo & Tao, GroundLM 2026)
ACL