InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914522431750144 |
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| author | Iyengar, Anirudh Iyengar Kaniyar Narayana Mukhopadhyay, Srija Qidwai, Adnan Singh, Shubhankar Roth, Dan Gupta, Vivek |
| author_facet | Iyengar, Anirudh Iyengar Kaniyar Narayana Mukhopadhyay, Srija Qidwai, Adnan Singh, Shubhankar Roth, Dan Gupta, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information Iyengar, Anirudh Iyengar Kaniyar Narayana Mukhopadhyay, Srija Qidwai, Adnan Singh, Shubhankar Roth, Dan Gupta, Vivek Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition I.2.7; I.2.10; I.4.10; I.7.5 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. |
| title | InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition I.2.7; I.2.10; I.4.10; I.7.5 |
| url | https://arxiv.org/abs/2508.07630 |