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Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation

Yufeng Li, Zaijing Li, F. Zhang, Zongzong Wu, Ming Zhao, Fengxiao Tang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29933 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

A geração de token aumentado por nota (RAG) freqüentemente integra gráficos de conhecimento (KGs) para resposta complexa a perguntas multi-hop. No entanto, seu desempenho é cada vez mais bloqueado pelos processos de estruturação e raciocínio do conhecimento. (i) Perda semântica grave: Distilando textos contínuos em símbolos discretos remove as nuances contextuais e a coerência narrativa. (ii) Discordância de modalidade pronunciada: Estruturas gráficas fragmentadas colidem com os textos contínuos, enquanto os LLMs são pré-treinados, enquanto reescrevê-los em linguagem natural introduz ruído gerador e omite informações de custo. Para abordar essas limitações, propomos ThreadRAG, um framework de evidência baseado em topologia semântica para estr

Machine translation (Meta M2M100); the English original follows.

Abstract

Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answering. However, their performance is increasingly bottlenecked by the knowledge structuring and reasoning processes. (i) Severe semantic loss: Distilling continuous texts into discrete symbols strips away contextual nuances and narrative coherence. (ii) Pronounced modality mismatch: Fragmented graph structures clash with the continuous texts LLMs are pre-trained on, while rewriting them into natural language introduces generative noise and omits information. To address these limitations, we propose ThreadRAG, a semantic-topology-based RAG framework for context-preserving knowledge structuring and modality-native evidence reasoning. ThreadRAG introduces a Semantic Topology Mapper (STM), which organizes retrieved documents into context-intact Semantic Nodes and constructs a Semantic Topology Graph (STG) through narrative, semantic, and lexical edges. It further incorporates Energy-Driven Pathfinding (EDP), which identifies query-relevant subgraphs via energy diffusion and deterministically linearizes them into coherent textual evidence paths. Experiments on three multi-hop QA benchmarks demonstrate that ThreadRAG consistently outperforms strong baselines. Notably, it achieves a 14.4% EM improvement over text-centric methods on HotpotQA and surpasses the best baseline by 5.8% EM on 2WikiMultihop, while reducing token cost to 23.3% of the strongest baseline.

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Topics: Computer science, Theoretical computer science, Structuring, Graph, Security token, Consistency (knowledge bases)

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