AutoEval: A Practical Framework for Autonomous Evaluation of Mobile Agents

• URL: http://arxiv.org/abs/2503.02403v1
• Date: Tue, 04 Mar 2025 08:44:30 GMT
• Title: AutoEval: A Practical Framework for Autonomous Evaluation of Mobile Agents
• Authors: Jiahui Sun, Zhichao Hua, Yubin Xia,
• Abstract summary: AutoEval is an autonomous agent evaluation framework that tests a mobile agent without any manual effort.<n>We implement a prototype of our framework and validate the automatically generated task reward signals, finding over 93% coverage to human-annotated reward signals.<n>We evaluate the state-of-the-art mobile agents using our framework, providing detailed insights into their performance characteristics and limitations.
• Score: 5.515875179998062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and systematic evaluation of mobile agents can significantly advance their development and real-world applicability. However, existing benchmarks for mobile agents lack practicality and scalability due to the extensive manual effort required to define task reward signals and implement corresponding evaluation codes. To this end, we propose AutoEval, an autonomous agent evaluation framework that tests a mobile agent without any manual effort. First, we design a Structured Substate Representation to describe the UI state changes while agent execution, such that task reward signals can be automatically generated. Second, we utilize a Judge System that can autonomously evaluate agents' performance given the automatically generated task reward signals. By providing only a task description, our framework evaluates agents with fine-grained performance feedback to that task without any extra manual effort. We implement a prototype of our framework and validate the automatically generated task reward signals, finding over 93% coverage to human-annotated reward signals. Moreover, to prove the effectiveness of our autonomous Judge System, we manually verify its judge results and demonstrate that it achieves 94% accuracy. Finally, we evaluate the state-of-the-art mobile agents using our framework, providing detailed insights into their performance characteristics and limitations.

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