# ChartSync: A Benchmark for Visuo-Logical Cascading Chart Editing

arXiv: https://arxiv.org/abs/2607.10301 · HTML: https://mlsanity.com/paper/2607.10301/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 11 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing](https://mlsanity.com/paper/2306.10012/) (2023)
- [Seedream 4.0: Toward Next-generation Multimodal Image Generation](https://mlsanity.com/paper/2509.20427/) (2025)
- [I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing](https://mlsanity.com/paper/2408.14180/) (2024)
- [Qwen-Image-2.0 Technical Report](https://mlsanity.com/paper/2605.10730/) (2026)
- [ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing](https://mlsanity.com/paper/2505.11935/) (2025)
- [ICE-Bench: A Unified and Comprehensive Benchmark for Image Creating and Editing](https://mlsanity.com/paper/2503.14482/) (2025)
- [DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models](https://mlsanity.com/paper/2512.24165/) (2025)
- [Wan-Image: Pushing the Boundaries of Generative Visual Intelligence](https://mlsanity.com/paper/2604.19858/) (2026)
- [ChartEditor: A Reinforcement Learning Framework for Robust Chart Editing](https://mlsanity.com/paper/2511.15266/) (2025)
- [Charts Are Not Images: On the Challenges of Scientific Chart Editing](https://mlsanity.com/paper/2512.00752/) (2025)
- [RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions](https://mlsanity.com/paper/2506.03448/) (2025)
- [TextEditBench: Evaluating Reasoning-aware Text Editing Beyond Rendering](https://mlsanity.com/paper/2512.16270/) (2025)
- [EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models](https://mlsanity.com/paper/2603.12252/) (2026)
- [OmniText: A Training-Free Generalist for Controllable Text-Image Manipulation](https://mlsanity.com/paper/2510.24093/) (2025)

## Built on by (most cited first, 4 of 4)

- [Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing](https://mlsanity.com/paper/2609.08657/) (2026)
- [ChartRevise: A Dataset and Evaluation Protocol for Exact Chart Editing via Code](https://mlsanity.com/paper/2609.38642/) (2026)
- [Beyond Plausibility: Verifiable Fine-Grained Image Editing on Structured Assets](https://mlsanity.com/paper/2610.04381/) (2026)
- [Pixels or Code? Benchmarking Instruction-Guided Visual Artifact Operation](https://mlsanity.com/paper/2610.16444/) (2026)

## Abstract (arXiv)

Generative image editing models struggle with structured statistical charts when data modifications require geometric synchronization. We formalize this task as Visuo-Logical Cascading Editing (VLCE). However, existing methods remain confined to localized text substitutions and struggle with dependency-aware cascading updates. To systematically evaluate this capability, we introduce ChartSync, an expert-validated benchmark constructed via a programmatic rendering pipeline that guarantees deterministic visuo-logical coupling for the ground truth. ChartSync comprises 870 triplets across 9 chart categories and 4 task types, including 235 geometry-coupled VLCE instances that specifically test cascading text-to-geometry synchronization. We further evaluate these instances via a two-tier framework combining objective visual metrics with a vision-language model judge paradigm to assess low-level fidelity alongside multimodal comprehension and reasoning. Evaluating 14 image editing models and one code-mediated pipeline reveals a nuanced capability gap: most open-source models suffer severe drops in geometric synchronization, while only two frontier proprietary models show emerging VLCE capability, with their residual errors mainly involving semantic isolation and background corruption. Our detailed error analysis deconstructs these failure paradigms to identify core meta-abilities for guiding future multimodal architectures. The ChartSync dataset and code are publicly released at https://github.com/kaka-yjk/ChartSyncCodebase.