Information as Structural Alignment: A Dynamical Theory of Continual Learning

A research team has introduced a novel theoretical framework called the Informational Buildup Framework (IBF) to fundamentally address the problem of catastrophic forgetting in artificial intelligence, as detailed in a paper published on the arXiv preprint server on April 7, 2026. The work posits that forgetting is an inherent mathematical flaw in current AI models, which store all knowledge as overlapping patterns in shared parameters, and proposes a new dynamical systems approach to derive learning stability from the system's own internal processes. The core argument of the research is that catastrophic forgetting—where an AI model loses previously learned information when trained on new tasks—is not a mere technical bug but an unavoidable consequence of the dominant paradigm in machine learning. In this paradigm, knowledge from different tasks is superimposed onto the same global set of parameters within a neural network. The paper critiques existing mitigation techniques like regularization, replay of old data, and creating frozen sub-networks, arguing that these are external patches applied to a flawed foundation rather than solutions emerging from the system's intrinsic learning mechanics. The proposed Informational Buildup Framework offers a radical alternative. Instead of a static parameter space, it conceptualizes learning as a process of "structural alignment" within a dynamical system. In this model, knowledge is not stored as static weights but is embodied in the stable, self-reinforcing patterns of activity and connectivity that emerge and persist over time. The framework suggests that continual learning can be achieved by designing systems where new information aligns with and builds upon these existing dynamical structures, naturally preserving old knowledge while integrating the new, without the need for external corrective mechanisms. This theoretical shift could have profound implications for the future of AI, particularly in developing systems that learn continuously and adaptively in real-world environments, much like biological brains. By moving from a static storage model to a dynamic, process-oriented one, the IBF aims to create a more robust and natural foundation for lifelong machine learning. The paper, currently awaiting peer review, represents a significant contribution to the theoretical underpinnings of AI and could guide the design of next-generation learning algorithms.