4 accepted papers
Recently, Large Language Models (LLMs) based Web Agents have shown significant potential in web understanding and interaction tasks. However, their personalization ability and user experience remain limited by the ambiguity and dynamic nature of user intent, struggling to model diverse user interest
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, showcasing significant potential for long-term time series forecasting (LTSF), and consequently attracting substantial research interest. In LTSF, temporal decomposition has been widely adopted in existing…
Knowledge Distillation, an effective model compression technique, transfers knowledge from a large teacher model to a smaller student model, reducing computational costs while maintaining model performance. In large-scale retrieval tasks, maintaining the consistency of retrieval result rankings is c…
As the Internet’s complexity and information volume surge, the need for efficient web automation becomes critical. Traditional web agents struggle with redundant web content, which disrupts their understanding of the environment. They also face inefficiencies in multi-task scenarios due to handcraft…