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arXiv · 2606.23997

ChartWalker: Benchmarking the Cross-Chart RAG Task with Hierarchical Knowledge Graphs

Ning Tang·Chenghan Xie·Hanyang Yuan·Yi Li·Renhong Huang·Qian Kou·Xiaofeng Shi·Hua Zhou·Jiarong Xu
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Abstract

Cross-Chart Retrieval-Augmented Generation (RAG) is critical for complex multi-modal analytical tasks in scientific, business, and political domains. However, existing benchmarks either focus on tables, which are well-structured and textualized, or generate cross-chart questions by simply extracting key points, which often induces lexical overlap between queries and evidence and yields logically inconsistent reasoning chains. To address this, we introduce ChartWalker, a novel framework for constructing challenging cross-chart RAG tasks. ChartWalker features a hierarchical knowledge graph construction method tailored to charts, which organizes entities and relations by granularity to preserve analytical structure. We then propose a structure-aware sampling algorithm that synthesizes semantically coherent, multi-hop reasoning paths, enabling explicit control over query difficulty and granularity for QA generation. Built with this framework, we release ChartWalker-Bench, a comprehensive benchmark spanning diverse domains and cross-chart query types. Extensive evaluations across major RAG paradigms reveal significant performance gaps, underscoring the benchmark's difficulty and utility. Furthermore, we provide ChartWalker-Agent, an agentic baseline to facilitate analysis and inspire future system design.

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Ning Tang, Chenghan Xie, Hanyang Yuan, Yi Li, Renhong Huang, Qian Kou, Xiaofeng Shi, Hua Zhou, Jiarong Xu. 2026-06-22. ChartWalker: Benchmarking the Cross-Chart RAG Task with Hierarchical Knowledge Graphs. https://arxiv.org/abs/2606.23997

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