Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models

2026-04-13

AI Summary

Existing retrieval-augmented generation (RAG) approaches treat knowledge as an unordered set, overlooking the critical role of event sequencing in reasoning. This work proposes OKH-RAG, the first framework to explicitly incorporate sequence as a first-class structural attribute within a hypergraph-based RAG architecture. OKH-RAG models knowledge using priority-structured hypergraphs and introduces a sequential reasoning mechanism over hyperedges, coupled with a data-driven transition model that learns interaction orderings without explicit temporal supervision. Evaluated on sequence-sensitive tasks such as tropical cyclone forecasting and port operations, OKH-RAG substantially outperforms unordered baselines, demonstrating that explicitly modeling interaction sequences significantly enhances reasoning performance.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Acquisition
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including graph- and hypergraph-based approaches treat retrieved evidence as an unordered set, implicitly assuming permutation invariance. This assumption is misaligned with many real-world reasoning tasks, where outcomes depend not only on which interactions occur, but also on the order in which they unfold. We propose Order-Aware Knowledge Hypergraph RAG (OKH-RAG), which treats order as a first-class structural property. OKH-RAG represents knowledge as higher-order interactions within a hypergraph augmented with precedence structure, and reformulates retrieval as sequence inference over hyperedges. Instead of selecting independent facts, it recovers coherent interaction trajectories that reflect underlying reasoning processes. A learned transition model infers precedence directly from data without requiring explicit temporal supervision. We evaluate OKH-RAG on order-sensitive question answering and explanation tasks, including tropical cyclone and port operation scenarios. OKH-RAG consistently outperforms permutation-invariant baselines, and ablations show that these gains arise specifically from modeling interaction order. These results highlight a key limitation of set-based retrieval: effective reasoning requires not only retrieving relevant evidence, but organizing it into structured sequences.

Problem

• Retrieval-Augmented Generation
• Order Sensitivity
• Hypergraph
• Knowledge Representation
• Reasoning

Innovation

• order-aware retrieval
• hypergraph RAG
• sequence inference
• precedence structure
• interaction trajectory