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[2602.19240] Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering
Summary
The paper presents Topology-enhanced Retrieval-Augmented Generation (TopoRAG), a novel framework for improving reasoning in textual graph question answering by integrating higher-dimensional topological structures.
Why It Matters
This research addresses limitations in existing Retrieval-Augmented Generation models by incorporating multi-dimensional topological reasoning, which enhances the ability of Large Language Models to perform complex inferences over relational data. This advancement is crucial for applications requiring nuanced understanding of interconnected information.
Key Takeaways
- TopoRAG improves reasoning capabilities in textual graph tasks.
- The framework captures higher-dimensional topological dependencies.
- Empirical evaluations show TopoRAG outperforms existing models.
- Incorporating cycles in reasoning enhances contextual grounding.
- The approach addresses limitations of low-dimensional graph representations.
Computer Science > Artificial Intelligence arXiv:2602.19240 (cs) [Submitted on 22 Feb 2026] Title:Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering Authors:Sen Zhao, Lincheng Zhou, Yue Chen, Ding Zou View a PDF of the paper titled Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering, by Sen Zhao and 3 other authors View PDF HTML (experimental) Abstract:Retrieval-Augmented Generation (RAG) enhances the reasoning ability of Large Language Models (LLMs) by dynamically integrating external knowledge, thereby mitigating hallucinations and strengthening contextual grounding for structured data such as graphs. Nevertheless, most existing RAG variants for textual graphs concentrate on low-dimensional structures -- treating nodes as entities (0-dimensional) and edges or paths as pairwise or sequential relations (1-dimensional), but overlook cycles, which are crucial for reasoning over relational loops. Such cycles often arise in questions requiring closed-loop inference about similar objects or relative positions. This limitation often results in incomplete contextual grounding and restricted reasoning capability. In this work, we propose Topology-enhanced Retrieval-Augmented Generation (TopoRAG), a novel framework for textual graph question answering that effectively captures higher-dimensional topological and relational dependencies. Specifically, TopoRAG first lifts textual g...