Yacine Mansour, Hakim Ait Ahmed, 2026. "Neural Phase Transitions and the Emergence of Complex Computational Behaviors", Journal of Frontiers in Artificial Intelligence and Machine Learning 1(1): 47-63.

Neural phase transitions have become an important concept within the artificial intelligence research community, offering insights into how complex computational behaviors emerge in large-scale neural networks. Neural phase transitions, motivated by the sudden changes in the structure and dynamics of physical systems as critical thresholds are crossed, describe sudden shifts in performance capabilities and internal organization of artificial neural networks at higher scales, data-exposure or growth. Recent progress in deep learning suggests neural networks are prone to emergent behaviors that are not readily predictable from the properties of smaller models. These behaviors consist of sophisticated reasoning, in-context learning, knowledge retrieval, abstraction and problem-solving skills that emerge discontinuously only after crossing a computation threshold.
This paper explores the theory, mechanisms and consequences of neural phase transitions in contemporary AI systems. ABSTRACTWe investigate the critical changes in neural computation as a function of scaling parameters, training data, model architecture and information flow. Special emphasis is placed on the connection between phase transitions and generation of higherorder representations, adaptive strategies for learning, and complex reasoning processes. Additionally, this research also investigates how criticality, nonlinear dynamics and self-organization support the ability of neural networks to go from simple pattern recognizers to complex computational units that can perform a broad range of cognitive functions.
This indicates that neural phasetransitions may serve as a new explanatory lens for understanding emergent intelligence in large-scale models. When neural systems are near critical states of computation, organization, and representation, they achieve entirely new forms of information processing as well as markedly improving performance and capability. Recognizing these transitions is a key to anticipating these behaviors, enhancing the safety of advanced AI systems, optimizing architectures of interest, and designing more explainable and trustworthy intelligent systems. In the end, the story of phase transitions in neural networks provides a window on the emergent mechanisms of artificial cognition, and it takes us one step closer to a scientifically validated theory of collective computation in scale free complex systems such as deep networks.

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