Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent
Abstract
Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.
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BibTeX
@misc{xiao2026structural,
title = {{Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent}},
author = {Lizhi Xiao and Sihong Wu and Victoria Xiao and Yiqiao Song and Chen Gu and Jianwei Ma and Xinming Wu and Aimé Fournier},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.35400},
eprint = {2609.35400},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.35400},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent
AU - Lizhi Xiao
AU - Sihong Wu
AU - Victoria Xiao
AU - Yiqiao Song
AU - Chen Gu
AU - Jianwei Ma
AU - Xinming Wu
AU - Aimé Fournier
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.35400
UR - https://arxiv.org/abs/2609.35400
AB - Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.
ER -