DiagramBank: A Large-scale Dataset of Diagram Design Exemplars with Paper Metadata for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2604.20857v1
• Date: Sat, 28 Feb 2026 04:17:35 GMT
• Title: DiagramBank: A Large-scale Dataset of Diagram Design Exemplars with Paper Metadata for Retrieval-Augmented Generation
• Abstract summary: We present DiagramBank, a large-scale dataset consisting of 89,422 schematic diagrams curated from top-tier scientific publications.<n>DiagramBank is developed through our automated curation pipeline that extracts figures and corresponding in-text references.<n>We release DiagramBank in a ready-to-index format and provide a retrieval-augmented generation to demonstrate exemplar-conditioned synthesis of teaser figures.
• Score: 4.248969416909163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in autonomous ``AI scientist'' systems have demonstrated the ability to automatically write scientific manuscripts and codes with execution. However, producing a publication-grade scientific diagram (e.g., teaser figure) is still a major bottleneck in the ``end-to-end'' paper generation process. For example, a teaser figure acts as a strategic visual interface and serves a different purpose than derivative data plots. It demands conceptual synthesis and planning to translate complex logic workflow into a compelling graphic that guides intuition and sparks curiosity. Existing AI scientist systems usually omit this component or fall back to an inferior alternative. To bridge this gap, we present DiagramBank, a large-scale dataset consisting of 89,422 schematic diagrams curated from existing top-tier scientific publications, designed for multimodal retrieval and exemplar-driven scientific figure generation. DiagramBank is developed through our automated curation pipeline that extracts figures and corresponding in-text references, and uses a CLIP-based filter to differentiate schematic diagrams from standard plots or natural images. Each instance is paired with rich context from abstract, caption, to figure-reference pairs, enabling information retrieval under different query granularities. We release DiagramBank in a ready-to-index format and provide a retrieval-augmented generation codebase to demonstrate exemplar-conditioned synthesis of teaser figures. DiagramBank is publicly available at https://huggingface.co/datasets/zhangt20/DiagramBank with code at https://github.com/csml-rpi/DiagramBank.

Related papers

• DiagramRAG: A Lightweight Framework to Retrieve Scientific Diagram for Figure Generation [9.701968199439387]
  We introduce DiagramRAG, a lightweight retrieval-augmented framework for sketch-based scientific diagram completion.<n>Given a user sketch, DiagramRAG retrieves reference diagrams that are both semantically relevant to the sketch content and topologically compatible with its structure.<n>Experiments show that DiagramRAG achieves F1-scores of 0.848 and 0.802 on DiagramBank and FigureBench, respectively, and improves generation quality with the best VLM-as-a-Judge score of 7.170.
  arXiv  Detail & Related papers  (2026-05-27T04:03:22Z)
• Graph-Anchored Knowledge Indexing for Retrieval-Augmented Generation [53.42323544075114]
  We propose GraphAnchor, a novel Graph-Anchored Knowledge Indexing approach.<n> Experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of GraphAnchor.
  arXiv  Detail & Related papers  (2026-01-23T05:41:05Z)
• Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphs [7.943264761730892]
  Multi-Agent GraphRAG serves as a natural language interface to LPG-based graph data.<n>Iterative content-aware correction and normalization, reinforced by an aggregated feedback loop, ensures both semantic and syntactic refinement of generated queries.<n>This highlights how such an approach can bridge AI with real-world applications at scale, enabling industrial digital automation use cases.
  arXiv  Detail & Related papers  (2025-11-11T14:04:00Z)
• RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning [51.393018266721576]
  We propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP)<n>Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem.<n>We introduce a strategy termed "BBox and Index as Visual Prompt" (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image.
  arXiv  Detail & Related papers  (2025-11-04T09:08:44Z)
• DiagramEval: Evaluating LLM-Generated Diagrams via Graphs [25.040934047462112]
  We argue that a promising direction is to generate demonstration diagrams directly in textual form as SVGs.<n>We propose DiagramEval, a novel evaluation metric designed to assess the quality of demonstration diagrams generated by large language models.
  arXiv  Detail & Related papers  (2025-10-29T17:56:17Z)
• ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning [64.4193334712998]
  The chart-to-code generation task requires MLLMs to convert chart images into executable code.<n>This task faces two main challenges: limited data diversity and the difficulty of maintaining visual consistency between generated charts and the original ones.<n>We propose ReChartPrompt, leveraging real-world, human-designed charts extracted from arXiv papers as prompts.<n>We also propose ChartSimRL, a GRPO-based reinforcement learning algorithm guided by a novel chart similarity reward.
  arXiv  Detail & Related papers  (2025-08-25T02:32:56Z)
• SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement [22.07623299712134]
  We propose SciDoc2Diagram, a task that extracts relevant information from scientific papers and generates diagrams. We develop a pipeline SciDoc2Diagrammer that generates diagrams based on user intentions using intermediate code generation.
  arXiv  Detail & Related papers  (2024-09-28T05:10:39Z)
• DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning [62.51232333352754]
  Text-to-image (T2I) generation has seen significant growth over the past few years. Despite this, there has been little work on generating diagrams with T2I models. We present DiagrammerGPT, a novel two-stage text-to-diagram generation framework. We show that our framework produces more accurate diagrams, outperforming existing T2I models.
  arXiv  Detail & Related papers  (2023-10-18T17:37:10Z)
• Scientific Paper Extractive Summarization Enhanced by Citation Graphs [50.19266650000948]
  We focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available.
  arXiv  Detail & Related papers  (2022-12-08T11:53:12Z)
• A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features [61.92791503017341]
  Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data.
  arXiv  Detail & Related papers  (2022-06-16T22:46:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.