ChartAnchor: Chart Grounding with Structural-Semantic Fidelity

• URL: http://arxiv.org/abs/2512.01017v2
• Date: Mon, 08 Dec 2025 06:17:19 GMT
• Title: ChartAnchor: Chart Grounding with Structural-Semantic Fidelity
• Authors: Xinhang Li, Jingbo Zhou, Pengfei Luo, Yixiong Xiao, Tong Xu,
• Abstract summary: Chart grounding refers to the bidirectional alignment between a chart's visual appearance and the structured semantics.<n>ChartAnchor is a benchmark of 8k+ chart-table-code triples spanning 30 chart types drawn from diverse real-world and augmented sources.<n>A multi-level evaluation framework integrates semantic validation, stylistic analysis, and perceptual metrics to assess both structural and content-level correctness.
• Score: 19.798612765001746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in multimodal large language models (MLLMs) highlight the need for benchmarks that rigorously evaluate structured chart comprehension. Chart grounding refers to the bidirectional alignment between a chart's visual appearance and the structured semantics. This task requires models to produce a symbolic specification that faithfully captures the chart's visual and structural intent, while also recovering the underlying tabular data with precise values and relationships. Chart grounding directly reflects a model's capabilities in numerical reasoning, multimodal alignment, and structural reconstruction, and has several important applications in real-world scenarios. Existing benchmarks, constrained by narrow chart diversity, isolated tasks, and incomplete evaluation frameworks, fail to holistically assess grounding. To address this, we propose ChartAnchor, a comprehensive benchmark of 8k+ chart-table-code triples spanning 30 chart types drawn from diverse real-world and augmented sources. ChartAnchor introduces two complementary tasks: chart-to-code generation (synthesizing executable code to replicate charts) and controlled chart-to-table reconstruction (extracting exact data with predefined headers), enabling cross-validation of visual and numerical fidelity. A multi-level evaluation framework integrates semantic validation, stylistic analysis, and perceptual metrics to assess both structural and content-level correctness. Extensive experiments on MLLMs reveal critical limitations in numerical precision and code synthesis, emphasizing the need for structured reasoning beyond surface-level perception. By unifying symbolic and data-driven grounding, ChartAnchor establishes a rigorous foundation for chart grounding, offering meaningful insights for advancing MLLMs in scientific, financial, and industrial domains.

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