Advancements in deep learning have significantly improved model performance across tasks involving code, text, and image processing. However, these models still exhibit notable mispredictions in real-world applications, even when trained on up-to-date data. Such failures often arise from slight variations in inputs such as minor syntax changes in code, rephrasing in text, or subtle lighting shifts in images that reveal inherent limitations in these models’ capability to generalize effectively. Traditional approaches to address these challenges involve retraining, a resource-intensive process that demands significant investments in data labeling, model updates, and redeployment.

This research introduces an adaptive, on-the-fly input refinement framework aimed at improving model performance through input validation and transformation. The input validation component detects inputs likely to cause errors, while input transformation applies domain-specific adjustments to better align these inputs with the model’s handling capabilities. This dual strategy reduces mispredictions across various domains, boosting model performance without necessitating retraining. As a scalable and resource-efficient solution, this framework holds significant promise for high-stakes applications in software engineering, natural language processing, and computer vision.