arXiv:2605.29532v1 Announce Type: cross Abstract: Exploratory GUI testing is a particularly demanding setting for MLLM agents: without predefined test scripts, an agent must autonomously navigate an application and discover defects through its own interaction. However, current evaluation falls short on two fronts. First, existing benchmarks focus almost exclusively on interaction defects, leaving display defects outside the evaluation frame. Second, evaluation protocols are bound to predefined defect annotations, collapsing the testing process into a single end-state judgment that conflates qu
The proliferation of advanced MLLM agents for autonomous tasks is exposing the limitations of current evaluation methodologies, particularly in complex, exploratory environments.
Improved evaluation frameworks for AI agent testing are critical for ensuring reliability and accelerating the deployment of autonomous systems across various industries.
The focus on open-set evaluation for exploratory GUI testing will push the development of more robust and generalizable AI agents capable of handling unknown scenarios.
- · AI software developers
- · Automation companies
- · Software quality assurance
- · Traditional manual testers
- · Companies reliant on narrow AI testing
More reliable and adaptable AI agents for software interaction will emerge.
The cost and time required for software development and testing will decrease significantly.
AI agents could autonomously develop and maintain complex software systems with minimal human oversight.
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