Previously Generated Alternative

Research on alternative methods to established techniques in machine learning and related fields is actively exploring more efficient, robust, and reliable approaches. Current efforts focus on developing safer robot control strategies, improving the training of neural networks (including binary and graph neural networks), and enhancing the evaluation of large language models and other systems, particularly in safety-critical applications. These investigations aim to address limitations in existing methods, such as computational cost, vulnerability to adversarial attacks, and inadequate performance metrics, ultimately leading to more effective and trustworthy AI systems.