LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning

arXiv:2608.01328 · cs.CL, cs.AI, cs.CV · Submitted 2026-08-02 · Read on arXiv

Ziyan Xiao, Yinghao Zhu, Wenting Zhang, Heaju Kim, Lequan Yu

cs.CL, cs.AI, cs.CV

Submitted: 2026-08-02

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 46× in this paper · explained in 2 episodes
• Accuracy — 43× in this paper · explained in 1 episode
• MLLMs — 39× in this paper · explained in 2 episodes
• Robustness — 26× in this paper · explained in 6 episodes
• Grounding — 24× in this paper · explained in 3 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Benchmarking — 5× in this paper · explained in 4 episodes
• Human-in-the-loop — 4× in this paper · explained in 4 episodes
• Sufficiency — 4× in this paper · explained in 2 episodes
• Instruction Tuning — 3× in this paper · explained in 3 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Papers — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Vision-language models — 3× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Coherence — 1× in this paper · explained in 2 episodes
• Directed Acyclic Graph (DAG) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Inflation — 1× in this paper · explained in 2 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Performance Gap — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
• ChartLlama: A Multimodal LLM for Chart Understanding and Generation
• Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models
• Visual Self-Refine: A Pixel-Guided Paradigm for Accurate Chart Parsing
• In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding
• MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
• Kimi k1.5: Scaling Reinforcement Learning with LLMs
• POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering
• Rethinking Comprehensive Benchmark for Chart Understanding: A Perspective from Scientific Literature
• ChartBench: A Benchmark for Complex Visual Reasoning in Charts
• ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
• ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
• Multimodal Chain-of-Thought Reasoning in Language Models

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