Complexity Level
Complexity level, in various scientific domains, focuses on understanding and managing the challenges posed by intricate systems and data. Current research investigates this through diverse approaches, including developing algorithms to handle complex instructions in large language models, designing frameworks for efficient federated learning in hierarchical networks, and creating novel methods for measuring and evolving complexity in artificial systems. These advancements have significant implications for improving the performance and robustness of AI systems, optimizing resource utilization in distributed computing, and enhancing our understanding of cognitive processes and complex systems in general.
Papers
Is Complex Query Answering Really Complex?
Cosimo Gregucci, Bo Xiong, Daniel Hernandez, Lorenzo Loconte, Pasquale Minervini, Steffen Staab, Antonio Vergari
Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards
Grant C. Forbes, Leonardo Villalobos-Arias, Jianxun Wang, Arnav Jhala, David L. Roberts