Multihop

Multihop is a reasoning and information retrieval paradigm in artificial intelligence where systems must traverse multiple interconnected pieces of information or perform sequential logical steps to arrive at a final answer or conclusion. This approach requires AI models to chain together multiple facts, relationships, or reasoning steps from different sources rather than relying on single-step direct retrieval or inference. Multihop reasoning is essential for complex question answering, knowledge graph traversal, and problem-solving tasks that cannot be resolved through straightforward lookup or simple pattern matching. Modern implementations utilize techniques including iterative retrieval, graph neural networks, attention mechanisms, and chain-of-thought prompting to enable models to follow multi-step reasoning paths across distributed knowledge sources. Enterprise applications leverage multihop capabilities for complex document analysis, legal research, scientific literature review, and business intelligence tasks where answers require synthesizing information from multiple related sources. Advanced multihop systems incorporate memory mechanisms, dynamic retrieval strategies, and verification processes to ensure reasoning chains remain coherent and factually accurate across multiple inference steps. This capability enables AI systems to handle sophisticated analytical tasks that mirror human reasoning processes requiring integration of multiple pieces of evidence to reach comprehensive conclusions.