WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
WorldBench is a new multimodal reasoning benchmark emphasizing visual diversity. It builds a taxonomy of thousands of visual concepts, curates images from search engines and datasets, and designs questions that frontier MLLMs fail. Evaluation of 15 models shows the best achieves only 64.0% accuracy, highlighting the need for visual diversity in benchmarks.
[2606.06538] WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
[Submitted on 4 Jun 2026]
Title:WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
View a PDF of the paper titled WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark, by Yida Yin and 11 other authors
View PDF HTML (experimental)
Abstract:In real-world applications, models are expected to perform reliably across diverse settings. Yet, many existing multimodal benchmarks expand task types without capturing the visual diversity needed to handle open-ended visual inputs. We present WorldBench, a challenging and visually diverse reasoning benchmark to evaluate Multimodal Large Language Models (MLLMs). We build a taxonomy of thousands of visual concepts across multiple domains (e.g., living things). Guided by this taxonomy, we curate a broad collection of images from search engines and existing datasets to comprehensively represent the visual world. Through structured trial-and-error, we manually design challenging questions that frontier MLLMs fail to answer. On quantitative and human evaluations, WorldBench achieves higher visual diversity than any existing diverse benchmark. Evaluating 15 MLLMs on WorldBench reveals weaknesses in visual understanding: even the strongest model reaches only 64.0% accuracy, while some models perform marginally above chance-level. We hope our work highlights the importance of visual diversity in building multimodal benchmarks.
Comments: Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.06538 [cs.CV]
(or arXiv:2606.06538v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.06538
arXiv-issued DOI via DataCite
Submission history
From: Harish Krishnakumar [view email] [v1] Thu, 4 Jun 2026 01:11:21 UTC (41,062 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark, by Yida Yin and 11 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)