arXiv (All)
open access
DRAGON: A Benchmark for Evidence-Grounded Visual Reasoning over Diagrams
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 guarante
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- VGR: Visual Grounded Reasoning#189286
- GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning#630159
- GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning#628297
- GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models#973521
- Describe Anything Model for Visual Question Answering on Text-rich Images#1110103
Record · ID 1118533
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive