arXiv cs.AI· Ruairidh M. Battleday, Kai Sandbrink, Jimi Cullen-Drohan, Zihan Yan, Timothy Muller, Clare Maguire, Ales Kubicek, Fraser Greenlee-Scott, Sukrit Sumant, Tri Dao, J\"urgen Schmidhuber, Michal Valko, Joshua Tenenbaum, Thomas L. Griffiths, Zeb Kurth-Nelson, James C. R. Whittington·· 5 小时前AI 评分45
DiG-bench：游戏中的科学发现基准发布
DiG-bench: Discovery in Games
AI 导读
研究团队发布 DiG-bench（Discovery in Games）基准，用于评估 AI 智能体在目标未知的环境中通过交互与实验发现规则的能力。该基准包含 70 个独立游戏，每个游戏编码为短字符串并具有独特的变换规则，设置七个难度等级，最高难度可挑战最强模型；全部 70 个游戏均至少有一名人类首次尝试即通过。其中 21 个游戏公开发布，其余保持私有用于安全评测。
正文 · 原文
Authors:Ruairidh M. Battleday, Kai Sandbrink, Jimi Cullen-Drohan, Zihan Yan, Timothy Muller, Clare Maguire, Ales Kubicek, Fraser Greenlee-Scott, Sukrit Sumant, Tri Dao, Jürgen Schmidhuber, Michal Valko, Joshua Tenenbaum, Thomas L. Griffiths, Zeb Kurth-Nelson, James C.R. Whittington
Abstract:Discovery---formulating novel generalizations---is a central part of the scientific process. Despite its importance, there is a gap in the current AI benchmark landscape, with few benchmarks directly probing the capacity for discovering new knowledge with experimentation in controlled environments where the objective is unknown. To address this gap, we release a new benchmark: DiG-bench (Discovery in Games). DiG-bench consists of a set of 70 independent games. Each game is encoded as a short string and has unique transformation rules that must be discovered through interaction and experimentation. The levels of the game present a series of challenges to test whether the rules have been discovered, where the win conditions for each level are also unknown. We provide games at seven tiers of difficulty for AI agents. The lowest tier is routinely solvable by multiple models, while the highest tier challenges the best models in agentic harnesses. All 70 games were solved by at least one human on first attempt. A subset of 21 games is released publicly, and the remainder is held private for secure evaluation.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.12593 [cs.AI] |
| (or arXiv:2608.12593v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12593 arXiv-issued DOI via DataCite |
Submission history
From: James Whittington [view email]
[v1]
Wed, 12 Aug 2026 21:06:06 UTC (820 KB)
[v2]
Tue, 27 Oct 2026 21:28:33 UTC (819 KB)
来源：arXiv cs.AI · arxiv.org