GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity and rarely evaluate agents on completing entire storylines. To address this, we introduce FlashAdventure, a benchmark of 34 Flash-based adventure games designed to test full story arc completion and tackle the observation-behavior gap: the challenge of remembering and acting on earlier gameplay information. We also propose CUA-as-a-Judge, an automated gameplay evaluator, and COAST, an agentic framework leveraging long-term clue memory to better plan and solve sequential tasks. Experiments show current GUI agents struggle with full story arcs, while COAST improves milestone completion by bridging the observation-behavior gap. Nonetheless, a marked discrepancy between humans and best-performing agents warrants continued research efforts to narrow this divide.
@article{arxiv.2509.01052,
title = {FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games},
author = {Jaewoo Ahn and Junseo Kim and Heeseung Yun and Jaehyeon Son and Dongmin Park and Jaewoong Cho and Gunhee Kim},
journal= {arXiv preprint arXiv:2509.01052},
year = {2025}
}