Hidir Selcuk Nogay | Searcharxiv

arXiv2026

AI-Native Autonomous Infrastructure (ANAI): A Formal Framework for the Next General-Purpose Technology

Artificial intelligence is increasingly described as a candidate next generation general purpose technology (GPT). However, existing interpretations predominantly emphasize performance scaling rather than structural transformation. This paper introduces a formal framework for evaluating AI as a systemic infrastructural transition rather than merely a computational breakthrough. We propose the concept of AI Native Autonomous Infrastructure (ANAI), defined as a regime in which decision autonomy becomes embedded within critical infrastructures. The framework operationalizes this transition through three quantitative constructs: the Autonomy Index (AIx), the Infrastructure Coupling Coefficient (ICC), and the Technological Transition Potential (TTP). We formalize the joint scaling dynamics of autonomy and infrastructural embedding, derive threshold conditions for paradigm transition, and introduce a phase-space representation of systemic transformation. A temporal transition model further illustrates how nonlinear coevolution between autonomy and infrastructure integration produces super linear growth in transition potential. Unlike prior GPT cycles, the ANAI regime exhibits a recursive energy computation feedback loop in which AI systems both increase computational demand and optimize the infrastructures that sustain them. This feedback mechanism accelerates infrastructural embedding and differentiates AI driven transformation from previous technological revolutions. By shifting analytical focus from model performance to infrastructural autonomy and coupling intensity, this study offers a conceptual and mathematical foundation for assessing whether artificial intelligence constitutes the next general purpose technology.

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arXiv2026

A Hidden Markov Framework for Physically Interpretable Arc Stability Dynamics in Welding Systems

Electric arc welding (EAW) exhibits strongly non stationary and temporally evolving behavior, making reliable assessment of arc stability difficult using conventional frame based approaches. In this study, arc dynamics are modeled as a sequence of latent operational regimes within a probabilistic state-space framework. The welding current signal is transformed into a time-frequency domain using Short-Time Fourier Transform (STFT), and a set of physically meaningful spectral descriptors, including energy, entropy, and centroid, is extracted to construct the observation sequence. A Hidden Markov Model (HMM) is employed to capture temporal dependencies and estimate the evolution of arc states. The analysis reveals three dominant regimes, transient, stable, and extinction, with a clear monotonic increase in spectral energy and a corresponding decrease in entropy, indicating reduced variability under stable conditions. Despite partial overlap in the feature space, the inferred state sequence exhibits strong temporal coherence, supported by high state persistence and low transition rates. These findings highlight the limitations of static classification and emphasize the importance of temporal modeling. The proposed framework provides an interpretable and physically consistent representation of arc behavior, enabling more realistic monitoring and analysis of stability dynamics in welding processes.

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