The paper investigates how vision‑language models (VLMs) perceive uncertainty in visualizations and make decisions based on visual data. Two studies were conducted: one on agents’ ability to quantify uncertainty in charts, and another on their decision‑making when presented with images. Results show GPT‑4o best perceives uncertainty in pie and bar charts, while agents align most closely with rational decision theory when uncertainty is shown via circle charts, icon arrays, or text.
arXiv:2505.17202v2 Announce Type: replace-cross Abstract: Data visualization understanding, which requires integrating visual, numerical, and linguistic inputs, poses fundamental challenges to multim...
We adapt Stevens's power law to measure the innate ability of AI models to read visualizations, which can reveal the built-in perceptual mechanisms of algorithmic models. In our pilot study, models se...
arXiv:2610.08365v1 Announce Type: new Abstract: We adapt Stevens's power law to measure the innate ability of AI models to read visualizations, which can reveal the built-in perceptual mechanisms of...
The paper adapts Stevens's power law to assess how AI models, specifically GPT‑5.5, implicitly interpret visualizations. In a pilot study, the model first estimates the magnitude of a reference visual representation and then compares subsequent images of the same representation, without access to legends or colormap names. The evaluation covers twelve visual variables, aiming to make algorithmic reading of visual encodings measurable, comparable to human perception, and more transparent.
arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).
The paper introduces Humanity's Sixth Sense (HSS), a benchmark designed to evaluate intuitive visual reasoning in multimodal large language models. HSS covers a wide range of image and video inputs, organizes them under a structured taxonomy, and pairs each with human‑written prompts that probe implicit temporal, spatial, social, and abstract cues that humans can infer at a glance. Current state‑of‑the‑art models, including GPT‑6‑astra, achieve far lower accuracy than humans (53.6 % vs. 93.1 %), highlighting a significant gap in intuitive visual understanding.
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:2205. 04599v2 Announce Type: replace-cross Abstract: Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians and patients to interpret.
arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.
arXiv:2608. 05864v1 Announce Type: new Abstract: Large language models are increasingly applied as autonomous decision-making agents.
Large language models are increasingly applied as autonomous decision-making agents. However, in executive business decisions, existing benchmarks are limited to textonly settings.
arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.