Cognitive Entropy Alignment Network (Cean): A Self-balancing Neural Framework for Adaptive Reasoning

Even contemporary neural systems continue to depend on fixed training regimes and static optimization objectives. These neural systems install optimization-based. These systems begin to malfunction as a result of external influence. System feedback indicates a higher level of control and neural systems malfunction due to a lack of feedback controlling integration of neural systems. Control and exploitation feedback. A CEAN Network realizes a new neural control paradigm. Control feedback is dynamic as CEAN networks utilize entropy in neural optimization as a regularization signal. The CEAN maintains dual feedback systems via stability control and entropy control. The Stability Controller constrains representational variance when latent distributions drift toward excessive randomness, while the Entropy Enhancer injects back a structured form of stochasticity when over-determined features are reducing adaptability. The two subsystems maintain a kind of balance that dynamically aligns cognitive entropy of the neural network, allowing the network to perform well in changing and uncertain environments. Python-based programming using the PyTorch framework has shown CEAN to be an even better tool for testing generalization across various domains within standard datasets such as CIFAR-10→CIFAR-100, Visual Question Answering, and textual entailment tasks. The network therefore shows faster convergence, higher retention of representational diversity, and interpretable entropy trajectories associated with reasoning stability and suggest the entropy alignment establishes a new theoretical framework for autonomous self-balanced learning-a road towards an adaptive and explainable intelligence.