TopoChunker: Topology-Aware Agentic Document Chunking Framework

• URL: http://arxiv.org/abs/2603.18409v1
• Date: Thu, 19 Mar 2026 02:15:10 GMT
• Title: TopoChunker: Topology-Aware Agentic Document Chunking Framework
• Abstract summary: TopoChunker is an agentic framework that maps heterogeneous documents onto a Structured Intermediate Representation (SIR)<n>To balance structural fidelity with computational cost, TopoChunker employs a dual-agent architecture.<n>It outperforms the strongest LLM-based baseline by 8.0% in absolute generation accuracy and achieves an 83.26% Recall@3, while simultaneously reducing token overhead by 23.5%.
• Score: 5.304983617085637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current document chunking methods for Retrieval-Augmented Generation (RAG) typically linearize text. This forced linearization strips away intrinsic topological hierarchies, creating ``semantic fragmentation'' that degrades downstream retrieval quality. In this paper, we propose TopoChunker, an agentic framework that maps heterogeneous documents onto a Structured Intermediate Representation (SIR) to explicitly preserve cross-segment dependencies. To balance structural fidelity with computational cost, TopoChunker employs a dual-agent architecture. An Inspector Agent dynamically routes documents through cost-optimized extraction paths, while a Refiner Agent performs capacity auditing and topological context disambiguation to reconstruct hierarchical lineage. Evaluated on unstructured narratives (GutenQA) and complex reports (GovReport), TopoChunker demonstrates state-of-the-art performance. It outperforms the strongest LLM-based baseline by 8.0% in absolute generation accuracy and achieves an 83.26% Recall@3, while simultaneously reducing token overhead by 23.5%, offering a scalable approach for structure-aware RAG.

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