Information as Structural Alignment: A Dynamical Theory of Continual Learning

Researchers introduce the Informational Buildup Framework (IBF), a new approach to continual learning that eliminates catastrophic forgetting by treating information as structural alignment rather than stored parameters. The framework demonstrates superior performance across multiple domains including chess and image classification, achieving near-zero forgetting without requiring raw data replay.

The paper addresses a fundamental challenge in machine learning: catastrophic forgetting, where neural networks lose previously learned knowledge when trained on new tasks. Traditional solutions—regularization, experience replay, and frozen subnetworks—operate as external patches applied to standard parameter-based architectures. IBF proposes a paradigm shift by reconceptualizing how knowledge itself is represented and maintained.

The framework's theoretical foundation rests on two core mechanisms: a Law of Motion that directs system configurations toward higher coherence, and Modification Dynamics that continuously reshape the learning landscape in response to localized errors. This bottom-up approach generates memory, agency, and error correction as emergent properties rather than engineered components. The distinction proves significant because it suggests catastrophic forgetting stems from architectural limitations rather than implementation details.

Experimental validation spans diverse problem domains. In controlled non-stationary environments, IBF achieves 43% less forgetting than replay methods while eliminating data storage requirements. Chess evaluation through independent Stockfish analysis shows positive backward transfer (+38.5 centipawns), indicating improved performance on earlier tasks when learning new ones. Split-CIFAR-100 results demonstrate near-zero forgetting (BT = -0.004) with a frozen Vision Transformer encoder, suggesting compatibility with modern deep learning architectures.

The technical achievement carries implications for resource-constrained applications and privacy-sensitive domains where data retention proves problematic. By decoupling knowledge retention from data storage, IBF addresses practical deployment concerns while advancing theoretical understanding of learning dynamics. The framework's generalization across domains suggests potential applicability to reinforcement learning and continual adaptation problems beyond supervised classification.

• →IBF treats information as structural alignment rather than stored parameters, fundamentally reconceptualizing how neural networks maintain knowledge across tasks
• →The framework achieves near-zero forgetting on CIFAR-100 and positive backward transfer in chess without requiring raw data replay or external regularization mechanisms
• →Memory and self-correction emerge from learning dynamics rather than being engineered as separate modules, suggesting catastrophic forgetting is mathematically inherent to parameter superposition
• →Validation across three diverse domains—controlled environments, chess analysis, and image classification—demonstrates broad applicability beyond traditional benchmarks
• →The approach addresses privacy and resource constraints by eliminating data storage requirements while maintaining superior retention compared to replay-based methods