@inproceedings{ahn-etal-2025-flashadventure,
title = "{F}lash{A}dventure: A Benchmark for {GUI} Agents Solving Full Story Arcs in Diverse Adventure Games",
author = "Ahn, Jaewoo and
Kim, Junseo and
Yun, Heeseung and
Son, Jaehyeon and
Park, Dongmin and
Cho, Jaewoong and
Kim, Gunhee",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1192/",
pages = "23365--23395",
ISBN = "979-8-89176-332-6",
abstract = "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."
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%0 Conference Proceedings
%T FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games
%A Ahn, Jaewoo
%A Kim, Junseo
%A Yun, Heeseung
%A Son, Jaehyeon
%A Park, Dongmin
%A Cho, Jaewoong
%A Kim, Gunhee
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ahn-etal-2025-flashadventure
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1192/
%P 23365-23395
Markdown (Informal)
[FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games](https://aclanthology.org/2025.emnlp-main.1192/) (Ahn et al., EMNLP 2025)
ACL