Modular Reasoning

Modular reasoning in artificial intelligence focuses on decomposing complex reasoning tasks into smaller, manageable sub-tasks processed by independent modules. Current research emphasizes developing architectures that dynamically select and combine these modules, often integrating neural networks with symbolic reasoning or external knowledge bases, as seen in multi-stage systems and object-centric approaches. This modular approach aims to improve the robustness, interpretability, and generalization capabilities of AI systems, particularly in challenging domains like video question answering and reinforcement learning, leading to more efficient and effective problem-solving.