Thought Empowers Transformer

"Thought empowers Transformer" research explores enhancing the capabilities of transformer-based models by modifying their architecture or training methods. Current efforts focus on improving efficiency (e.g., through decoupled attention mechanisms), addressing limitations in handling sequential information (e.g., via dynamic position encoding), and enabling new functionalities like lifelong learning and privacy-preserving inference. These advancements aim to broaden the applicability of transformers to complex tasks, particularly in areas like 3D reconstruction, image generation, and symbolic reasoning, while simultaneously improving their computational efficiency and robustness.