Multi Hop

Multi-hop refers to reasoning or information retrieval processes that require multiple sequential steps or “hops” through different data sources, documents, or logical connections to reach a final answer or conclusion. This approach is essential for complex question-answering tasks where no single source contains complete information, requiring systems to gather and synthesize evidence from multiple locations. Multi-hop reasoning involves iterative information gathering, where each step informs the next query or reasoning operation, creating chains of logical dependencies. Common applications include multi-document question answering, knowledge graph traversal, and complex problem-solving that requires connecting disparate facts. Implementation techniques include graph neural networks, attention mechanisms that track reasoning paths, and retrieval-augmented generation with iterative refinement. For AI agents, multi-hop capabilities enable sophisticated analytical tasks, comprehensive research automation, and complex decision-making that mirrors human reasoning patterns by building conclusions through systematic evidence accumulation.