Papers with VisualWebArena
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks (2024.acl-long)
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Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, Daniel Fried
| Challenge: | Existing benchmarks focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. |
| Approach: | They propose a benchmark to assess the performance of multimodal web agents . they use visual and textual inputs to process and interpret natural language instructions . |
| Outcome: | a new benchmark assesses the performance of multimodal agents on visually grounded tasks . the benchmark identifies limitations of text-only agents and offers insights towards building stronger agents for the web . |
Attacking Vision-Language Computer Agents via Pop-ups (2025.acl-long)
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| Challenge: | Existing tools for analyzing and testing VLMs are lacking in understanding what types of attacks are possible and what types are effective. |
| Approach: | They propose to integrate pop-ups into existing agent testing environments to attack VLM agents by ignoring them. |
| Outcome: | The proposed attack success rate is 86% and decreases by 47% when integrating pop-ups into existing agent testing environments. |