However, such architectures often fail to deliver solid predictive performance on real-world tasks, limiting their practical impact. In contrast, global attention-based models such as graph transformers demonstrate strong performance in practice.

However, approximating policies using deep neural networks makes it difficult to interpret the learned decision-making process.

Recent advances in large language models have shown capabilities that are extraordinary and near-superhuman.

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging.

Fed-SVT is nearly optimal up to logarithmic factors.

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.