Multi-Hop Reasoning

The core engine breaks a complex query into a sequence of simpler sub-questions. Each answer becomes the context for the next retrieval step. This is often implemented as a chain-of-thought process guided by a planner agent.

• Decompose: 'What year was the inventor of the telephone born?' becomes [Who invented the telephone?] -> [When was Alexander Graham Bell born?]
• Dynamic Re-planning: The system must be able to revise its plan if an intermediate retrieval returns null or contradictory information.

Unlike stateless single-shot RAG, multi-hop systems maintain a working memory that accumulates retrieved facts across hops. This prevents information loss and allows the final synthesis step to reason over the complete chain of evidence.

• Mechanism: A context buffer is populated iteratively, often with metadata tracking the provenance of each fact.
• Challenge: Managing context window limits requires aggressive re-ranking and summarization of intermediate results.

Reasoning paths often mirror the structure of an underlying knowledge graph or entity network. The system traverses from one entity node to another via explicit relationships, rather than relying on unstructured semantic similarity alone.

• Entity Linking: Intermediate answers are resolved to unique IDs to enable precise graph traversal.
• Path Validation: The system scores the logical coherence of the entire reasoning path, not just the relevance of individual documents.

The final output is not a summary of a single document, but a synthesized answer that explicitly connects facts from multiple sources into a coherent, logical narrative. This requires a generation step that can perform multi-document fusion.

• Citation Spanning: The final answer must link each distinct claim back to its specific source document within the chain.
• Contradiction Resolution: The system must detect and resolve factual conflicts between documents retrieved at different hops.

Multi-hop systems introduce a fundamental performance tension. Each sequential retrieval hop adds linear latency, making them significantly slower than parallel retrieval. Architectures must balance depth of reasoning with time-to-first-token requirements.

• Speculative Retrieval: Fetching documents for potential next hops in parallel before the current hop is fully resolved.
• Early Stopping: Implementing confidence thresholds to terminate the reasoning chain once sufficient evidence is gathered.

A single hallucinated intermediate answer can poison the entire downstream reasoning chain. Robust systems implement fact-checking guardrails at each hop, using a Natural Language Inference (NLI) model to verify that a retrieved document entails the intermediate claim before proceeding.

• Entailment Scoring: A score is generated for each hop's factuality.
• Automatic Rollback: If a hop fails verification, the system triggers an alternative retrieval query or a web fallback.