Task Complexity

Task complexity research investigates how to effectively measure and model the difficulty of tasks for artificial intelligence systems, particularly large language models (LLMs). Current research focuses on developing robust benchmarks and evaluation frameworks that disentangle memorization from genuine generalization, often employing techniques like in-context learning and novel architectures such as graph neural networks and transformers with expressive attention mechanisms. Understanding task complexity is crucial for improving the reliability and efficiency of AI systems across diverse applications, from code generation and robotic manipulation to scientific discovery and personalized education.