标题： InterChart：跨分解和分布式图表信息的视觉推理基准 显示英文标题

标题： InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

摘要： 我们引入了InterChart，这是一个诊断基准，用于评估视觉-语言模型（VLMs）在多个相关图表之间进行推理的能力，这是科学报告、金融分析和公共政策仪表板等实际应用中的核心任务。 与以往专注于孤立、视觉统一图表的基准不同，InterChart通过多种问题类型对模型进行挑战，从实体推断和趋势相关性到数值估计以及基于2-3个主题或结构相关的图表的抽象多步骤推理。 我们将该基准分为三个难度递增的层级：(1) 单个图表上的事实推理，(2) 合成对齐图表集的整合分析，(3) 对视觉复杂的真实世界图表对进行语义推理。 我们对最先进的开源和闭源VLMs进行了评估，结果表明随着图表复杂性的增加，准确性出现一致且显著的下降。 我们发现，当我们将多实体图表分解为更简单的视觉单元时，模型表现更好，这突显了它们在跨图表整合方面的困难。 通过揭示这些系统性限制，InterChart提供了一个严格的框架，以推动复杂多视觉环境中的多模态推理发展。 显示英文摘要

摘要： We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task central to real-world applications such as scientific reporting, financial analysis, and public policy dashboards. Unlike prior benchmarks focusing on isolated, visually uniform charts, InterChart challenges models with diverse question types ranging from entity inference and trend correlation to numerical estimation and abstract multi-step reasoning grounded in 2-3 thematically or structurally related charts. We organize the benchmark into three tiers of increasing difficulty: (1) factual reasoning over individual charts, (2) integrative analysis across synthetically aligned chart sets, and (3) semantic inference over visually complex, real-world chart pairs. Our evaluation of state-of-the-art open and closed-source VLMs reveals consistent and steep accuracy declines as chart complexity increases. We find that models perform better when we decompose multi-entity charts into simpler visual units, underscoring their struggles with cross-chart integration. By exposing these systematic limitations, InterChart provides a rigorous framework for advancing multimodal reasoning in complex, multi-visual environments.