The ZTC-Judge-4B model is presented for verifying answers without generating tokens

Developers presented ZTC-Judge-4B — a specialized language model designed for automatic verification of answer correctness. The main feature of the architecture is the ability to conduct data verification without generating additional tokens, using only one pass. The model demonstrates high efficiency in evaluating answers to questions from professional exams. According to test results, the system achieved a score of 31 points, spending only 2 hours of computational time. This approach allows for significant reduction of delays in automated content checking. Despite successes in academic disciplines, tests revealed limitations in the neural network's work. The model shows low accuracy in analyzing content related to security questions in emergency situations. These data indicate the need to refine weights for specific domains where context interpretation accuracy is critical. The technical implementation of ZTC-Judge-4B is oriented towards scenarios where fast validation without redundant calculations is required. The absence of token generation makes the model a promising tool for filtering and quality assessment systems in real-time, where every processor cycle affects the overall service performance. Developers published the model's weights on the Hugging Face platform, providing access to the architecture for further testing and integration into quality assessment pipelines. Further development of the project will focus on addressing gaps in specialized knowledge areas, such as security protocols and critical instructions.