Joint Reasoning

Joint reasoning in artificial intelligence focuses on enabling AI systems to integrate information from multiple sources and reasoning modalities to solve complex problems, mirroring human cognitive processes. Current research emphasizes developing models that effectively fuse information from diverse sources like language models, knowledge graphs, and visual data, often employing techniques like chain-of-thought prompting, knowledge distillation, and graph neural networks to improve reasoning capabilities and explainability. This work is crucial for advancing AI's ability to handle nuanced tasks requiring multi-modal understanding and complex inference, with applications ranging from question answering and scientific fact-checking to human-AI collaboration and decision-making.