mlsanityData to 7 Oct 2026

Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents

arXiv 2505.24878 ↗First posted 30 May 2025cs.AI cs.CL cs.CVcited by 14

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

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Abstract (arXiv)

CAPTCHAs have been a critical bottleneck for deploying web agents in real-world applications, often blocking them from completing end-to-end automation tasks. While modern multimodal LLM agents have demonstrated impressive performance in static perception tasks, their ability to handle interactive, multi-step reasoning challenges like CAPTCHAs is largely untested. To address this gap, we introduce Open CaptchaWorld, the first web-based benchmark and platform specifically designed to evaluate the visual reasoning and interaction capabilities of MLLM-powered agents through diverse and dynamic CAPTCHA puzzles. Our benchmark spans 20 modern CAPTCHA types, totaling 225 CAPTCHAs, annotated with a new metric we propose: CAPTCHA Reasoning Depth, which quantifies the number of cognitive and motor steps required to solve each puzzle. Experimental results show that humans consistently achieve near-perfect scores, state-of-the-art MLLM agents struggle significantly, with success rates at most 40.0% by Browser-Use Openai-o3, far below human-level performance, 93.3%. This highlights Open CaptchaWorld as a vital benchmark for diagnosing the limits of current multimodal agents and guiding the development of more robust multimodal reasoning systems. Code and Data are available at this https URL.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 31 of its 51 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

31 of 51 references.

References · 31

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19 more
  1. 2015Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Modelscited by 1,352
  2. 2023MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilitiescited by 1,221
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Citing papers per year: 2025: 4; 2026: 10.

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  1. 2025Reasoning under Vision: Understanding Visual-Spatial Cognition in Vision-Language Models for CAPTCHA
  2. 2025MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks
  3. 2025CAPTURE: A Benchmark and Evaluation for LVLMs in CAPTCHA Resolving
  4. 2026VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference
  5. 2025BrowserArena: Evaluating LLM Agents on Real-World Web Navigation Tasks
  6. 2025MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Dialogue Evaluators
  7. 2025MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
  8. 2026BrowseComp-V3: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents