arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.
arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (...
arXiv:2609.26208v1 Announce Type: new Abstract: Data visualization is central to analytical reasoning, but real-world analysis increasingly requires language-driven interactive interfaces rather than...
SCAFFOLD is a large-scale structured dataset of computer science research figures, each paired with captions, context, questions, answers, and chain-of-thought reasoning traces. It contains 157,387 figure–question pairs from 3,058 arXiv papers, with subsets of 36,797 and 12,000 pairs for medium and small-scale use. The dataset was created using layout detection, PDF parsing, and AI-assisted question generation, and was used to benchmark a vision‑language model (Qwen2.5‑VL‑3B‑Instruct).
arXiv:2609.08657v1 Announce Type: cross Abstract: Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This str...
The paper introduces LayerWiseBench, a benchmark that evaluates visual language models on layer-wise chart understanding and editing. It focuses on three core concepts—layer attribution, layer binding, and visibility ordering—by pairing rendered charts with spatially aligned per-layer RGBA assets and functional role labels. The benchmark includes 2,800 charts, 7,329 understanding questions, and 53,791 editing variants, revealing that models excel at attribution and binding but struggle with visibility ordering, especially when editing overlapping components.
arXiv:2606. 29808v1 Announce Type: cross Abstract: Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis, retrieval, and redesign.
FigAct transforms static scientific figures into question‑conditioned visual presentations by acting directly on existing graphical elements. The framework generates short narrations, grounds each narration in visual evidence, and applies visual actions to guide viewer attention, mimicking a human presenter. A hierarchical search strategy reduces token usage by about 40×, and FigAct‑8B is trained with rewards for grounding accuracy, search efficiency, and rendering quality, evaluated on a human‑verified benchmark of real‑world scientific figures.
arXiv:2606. 29667v1 Announce Type: cross Abstract: The materials science literature encodes decades of experimental knowledge in figures, yet this visual record remains locked away and inaccessible to AI at scale.
The paper introduces SciGram, a large-scale dataset of 194K scientific diagrams paired with 1.4M visual instructions generated through a terminology‑grounded pipeline that extracts domain concepts, synthesizes facts, and retrieves relevant diagrams. Models fine‑tuned on SciGram show significant gains on diagram‑centric benchmarks such as TQA, ScienceQA, and AI2D, and when combined with existing models like LLaVA OneVision, set new state‑of‑the‑art performance. The authors release both the dataset and trained models to support further research in scientific diagram understanding.
arXiv:2609.13158v1 Announce Type: new Abstract: Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar...
arXiv:2606. 04244v1 Announce Type: new Abstract: Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then reason over the tool's output, specifically when they rely on visual aids.