Seal Generation

"SEAL" refers to a variety of research projects focusing on improving the capabilities of large language models (LLMs) and related AI systems. Current efforts concentrate on enhancing LLM performance in complex tasks, including long-horizon decision-making, API interaction, and robust scenario generation for autonomous systems, often employing techniques like hierarchical imitation learning, dual-encoder architectures, and adversarial training. These advancements are crucial for building more reliable and versatile AI systems with applications ranging from improved robotic manipulation and autonomous driving to more effective tools for data analysis and ecological monitoring.