AI struggles with long-term planning in big-budget video games
A data set tests AI planning across various game lengths and titles, showing limits and how to test responsibly.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Yiran Wang, Xingyilang Yin, Junfu Pu, Guangzhi Wang, Kaifeng Li, Mingyu Ouyang, Huiqiang Sun, Lingen Li, Cheng Cheng, Wangbo Yu, Honghao Chen, Xiaodong Cun, Chi-Man Pun, Zhiguo Cao, Ying Shan
- Universities and research institutions
- ARC Lab, Tencent, GVC Lab, Great Bay University, University of Macau, National University of Singapore, Huazhong University of Science and Technology, MMLab, CUHK
Institution metadata: Original paper ↗
What they did and found
Researchers built a test system with three parts: Annotations, Data, and a Benchmark. They collected 5,000 hours of big-budget game play, tagged actions and long goals, and tested 47 models. Results show planning for long tasks is hard, though longer prompts help somewhat; offline results align with some online outcomes.
Why it matters
Tests should cover longer goals with clear steps. Longer prompts can help plan future actions, and offline tests may hint at real play skill, though long plans stay difficult.
The study uses three parts: notes, data, and a test. It collected 5,000 hours of big-budget game play, linking videos and actions for a common model test.
Long planning remains risky. Giving models longer, structured goals helps them plan ahead, but real play still shows errors.
Original sources · 1
- GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay ↗arXiv · 2026-09-21
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Online results vary by game and setup; the study mainly covers large, commercial games and a common online variant.
What is your take?
Ask a question, add useful context or share a different perspective. Keep the conversation respectful and grounded.