ChartJudgeBench: Evaluating LMM Judges for Chart-to-Code Generation

arXiv:2609.24210 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Benchmark — 50× in this paper · explained in 2 episodes
• Alignment — 20× in this paper · explained in 1 episode
• Verification — 18× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Consistency — 15× in this paper · explained in 3 episodes
• Chart Perception Alignment — 9× in this paper
• Chart Reasoning Judgment — 5× in this paper
• Few-Shot Learning — 5× in this paper · explained in 4 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Coefficient of Variation — 3× in this paper
• Failure Modes — 3× in this paper · explained in 3 episodes
• alpha — 3× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Positional Bias — 2× in this paper · explained in 2 episodes
• Sycophancy — 2× in this paper · explained in 4 episodes
• Test-time Scaling — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
• Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation
• ChartEditor: A Reinforcement Learning Framework for Robust Chart Editing
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
• OpusAnimation: Code-Based Dynamic Chart Generation
• Visual-ERM: Reward Modeling for Visual Equivalence
• ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
• From Charts to Code: A Hierarchical Benchmark for Multimodal Models
• MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction
• Kimi-VL Technical Report
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
• MiMo-VL Technical Report
• VisJudge-Bench: Aesthetics and Quality Assessment of Visualizations
• LLaVA-Critic: Learning to Evaluate Multimodal Models
• Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction
• ChartM$^3$: Benchmarking Chart Editing with Multimodal Instructions
• Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement

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