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ARXIV:2604.25231 · VISUAL REASONING BENCHMARK · SUBMITTED 29 APR · 02:44 UTC · FRESHNESS STALE
ARXIV:2604.25231VISUAL REASONING BENCHMARKSUBMITTED 29 APR · 02:44 UTCFRESHNESS STALEAnirudh Iyengar Kaniyar Narayana Iyengar · Tampu Ravi Kumar · Gaurav Najpande · Manan Suri · Dinesh Manocha · Puneet Mathur · +1 at arXiv
DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models.
Opportunity summary
Pain DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct…
Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics, circuit schematics, and scientific diagrams. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in…
Visual Reasoning Benchmark moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models.
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10.48550/arXiv.2604.25231DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models.
Abstract
Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics, circuit schematics, and scientific diagrams. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions that support the prediction. Models may instead rely on textual correlations or dataset artifacts without identifying the visual evidence required to verify the answer. This limitation prevents reliable evaluation of diagram reasoning and reduces interpretability. We introduce DRAGON, a benchmark for evaluating evidence-grounded visual reasoning in diagrams. Given a diagram, a question, and the correct answer, a model must predict bounding boxes that correspond to the visual elements required to justify the answer. These evidence regions may include answer-bearing components, textual labels, legends, axes, connectors, and other supporting structures involved in the reasoning process. The DRAGON dataset contains 11,664 annotated question instances collected from six diagram QA datasets: ChartQA, Circuit-VQA, InfographicsVQA, MapIQ, MapWise, and AI2D. We release a 2,445-instance benchmark test set with human-verified reasoning evidence annotations and a standardized evaluation framework. We evaluate eight recent VLMs and analyze their ability to localize reasoning evidence across diverse diagram domains. DRAGON enables systematic evaluation of diagram reasoning and supports future research on models that ground their predictions in visual evidence.
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PROBLEM
DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guara...
METHOD
Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics, circuit schematics, and scientific diagrams. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct an...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions that support the pred...
WHY NOW
Visual Reasoning Benchmark moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions that support the prediction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics, circuit schematics, and scientific diagrams. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions that support the prediction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions that support the prediction. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Visual Reasoning Benchmark moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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DRAGON is a new benchmark and dataset for evaluating evidence-grounded visual reasoning in diagrams, addressing limitations of current vision-language models.
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