This protocol is often regarded as a "golden compass" for navigating SoT A detectors. But can these stand-out "winners" be

Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation.

With the advent of large language models (LLMs), reinforcement learning from human feedback (RLHF) has emerged as a pivotal technological paradigm to align models' behaviors with human values [

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications.

Multi-task offline reinforcement learning aims to develop a unified policy for diverse tasks without requiring real-time interaction with the environment.

Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts.

To avoid redundancy in these synthetic datasets, it is crucial that each element contains unique features and remains diverse from others during the synthesis stage. In this paper, we provide a thorough theoretical and empirical analysis of diversity within synthesized datasets. We argue that enhancing diversity can improve the parallelizable yet isolated synthesizing approach.