What is multi-hop reasoning?

Multi-hop reasoning refers to the ability of AI systems to make logical connections between different pieces of context in order to arrive at answers to questions or decisions. Rather than extracting an answer directly from one source, the system combines multiple supporting facts, inferences, and contextual relationships distributed across documents, knowledge bases, and other resources.

For example, a multi-hop question answering system might need to consult a passage describing a person's hometown, reference their alma mater in a different document, check enrollment numbers for that school, and synthesize this information across multiple steps to determine how large the person's hometown likely is. The system cannot simply extract the answer about hometown size directly, but rather must aggregate and reason over multiple supporting facts.

Multi-hop reasoning combines reading comprehension, logical reasoning, and knowledge integration. The AI system must deeply understand unstructured language, retrieve relevant facts, connect them logically, reconcile inconsistencies, and make indirect inferences. This allows it to answer questions and make decisions that require synthesizing distributed information and cascaded reasoning beyond what is directly stated.

Multi-hop reasoning moves toward more human-like understanding and decision making for AI systems. The ability to consult context, combine disparate information sources, and make logical connections is essential for robust intelligence. Multi-hop reasoning research aims to move beyond systems that simply retrieve facts, towards models that can comprehend relationships, draw inferences, and bring together diverse knowledge to solve problems.

This capability could unlock new applications in areas such as open-domain question answering, conversational AI, and hypothesis generation for complex tasks. Any domain requiring dynamic synthesis of evidence andfacts could benefit. Multi-hop reasoning also remains a challenge for current AI systems, suggesting continued research is needed to achieve more advanced, human-like reasoning.

The ability to aggregate and reason over disjointed information could be transformative for enterprise AI applications. Customer support chatbots with multi-hop reasoning could consult user profiles, transaction histories, and policy documents to resolve issues. Supply chain optimization systems could connect sales forecasts, inventory levels, logistics constraints, and other signals to identify risks and opportunities. Fraud detection could pull together clues from profiles, network graphs, and past patterns across varied sources.

By enabling systems to consult and synthesize distributed knowledge, multi-hop reasoning could drive insights, predictions, and recommendations beyond what current AI can produce. The challenge lies in making such reasoning robust and scalable to real-world ambiguity and complexity. But models capable of these dynamic inferences could provide great value to businesses across many domains.