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

• URL: http://arxiv.org/abs/2608.01328v1
• Date: Sun, 02 Aug 2026 15:48:04 GMT
• Title: LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning
• Abstract summary: Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities.<n>Existing benchmarks largely emphasize single-chart perception, while simple chart-to-chart connections are insufficient to evaluate these capabilities.<n>We introduce LongChart, a benchmark whose VQA sets contain an average of 6.5 images and 31.2 questions.<n>We evaluate 10 state-of-the-art MLLMs and examine three factors that influence performance: reasoning patterns, auxiliary tools, and robustness to image perturbations.
• Score: 25.04804547776196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities, enabling multi-chart understanding and multi-step inference. These abilities are increasingly important as MLLMs are adopted in complex agentic tasks. However, existing benchmarks largely emphasize single-chart perception, while simple chart-to-chart connections are insufficient to evaluate these capabilities. To capture multi-chart complexity while ensuring consistency and validity, we design a synthesis pipeline supported by latent graphs. Building on this pipeline, we introduce LongChart, a benchmark whose VQA sets contain an average of 6.5 images and 31.2 questions. We evaluate 10 state-of-the-art MLLMs and examine three factors that influence performance: reasoning patterns, auxiliary tools, and robustness to image perturbations. Our results show that MLLM accuracy decreases and varies substantially as computational complexity increases, highlighting directions for future research in multi-chart reasoning.

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