TopoChunker: Topology-Aware Agentic Document Chunking Framework
arXiv:2603.18409v1 Announce Type: new 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
arXiv:2603.18409v1 Announce Type: new 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.
Executive Summary
TopoChunker, a novel framework for topology-aware agentic document chunking, addresses the issue of semantic fragmentation in Retrieval-Augmented Generation (RAG) by preserving cross-segment dependencies. The dual-agent architecture, comprising an Inspector Agent and a Refiner Agent, optimizes extraction paths and reconstructs hierarchical lineages. Evaluated on unstructured narratives and complex reports, TopoChunker demonstrates state-of-the-art performance, outperforming the strongest LLM-based baseline by 8.0% in absolute generation accuracy. The framework's scalability and reduced token overhead make it a promising approach for structure-aware RAG. However, its applicability to diverse domains and potential computational costs require further investigation.
Key Points
- ▸ TopoChunker preserves cross-segment dependencies in document chunking
- ▸ Dual-agent architecture optimizes extraction paths and reconstructs hierarchical lineages
- ▸ State-of-the-art performance on unstructured narratives and complex reports
Merits
Strength in Preserving Cross-Document Relationships
TopoChunker's ability to preserve cross-document relationships enhances the accuracy and coherence of generated text.
Scalability and Reduced Token Overhead
The framework's scalability and reduced token overhead make it a practical solution for large-scale document processing.
Demerits
Limited Generalizability to Diverse Domains
The framework's performance may not generalize well to diverse domains, requiring further adaptation and fine-tuning.
Potential Computational Costs
The dual-agent architecture may incur significant computational costs, particularly for large documents or complex queries.
Expert Commentary
TopoChunker's innovative approach to topology-aware agentic document chunking addresses a significant limitation in current RAG methods. However, its applicability to diverse domains and potential computational costs require careful consideration. Future research should focus on generalizing the framework to various domains and optimizing its performance for large-scale document processing. Additionally, the implications of TopoChunker for information retrieval and management policies warrant further investigation.
Recommendations
- ✓ Investigate the generalizability of TopoChunker to diverse domains and fine-tune the framework for specific applications.
- ✓ Optimize the dual-agent architecture to reduce computational costs while maintaining performance.
