Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the ability to manage timing, tempo and simultaneous speaking, remains a critical and unevaluated challenge for conversational fluency. To address this gap, we introduce the Game-Time Benchmark, a framework to systematically assess these temporal capabilities. Inspired by how humans learn a language through language activities, Game-Time consists of basic instruction-following tasks and advanced tasks with temporal constraints, such as tempo adherence and synchronized responses. Our evaluation of diverse SLM architectures reveals a clear performance disparity: while state-of-the-art models handle basic tasks well, many contemporary systems still struggle with fundamental instruction-following. More critically, nearly all models degrade substantially under temporal constraints, exposing persistent weaknesses in time awareness and full-duplex interaction. The Game-Time Benchmark provides a foundation for guiding future research toward more temporally-aware conversational AI. Demos and datasets are available on our project website https://ga642381.github.io/Game-Time.

翻译：对话式口语语言模型（SLM）正成为实时语音交互的一种有前景范式。然而，其时间动态特性，包括管理时机、语速和同时说话的能力，仍是交互流畅性中关键且未得到评估的挑战。为弥补这一空白，我们引入游戏时刻基准（Game-Time Benchmark），这是一个系统性地评估这些时间能力的框架。受人类通过语言活动学习语言的启发，游戏时刻包含基础指令跟随任务和具有时间约束的进阶任务，例如语速遵从和同步响应。我们对不同SLM架构的评估揭示了明显的性能差异：尽管最先进的模型能很好地处理基础任务，但许多现有系统仍在基本的指令跟随上存在困难。更关键的是，几乎所有模型在时间约束下性能都显著下降，暴露出时间感知和全双工交互方面的持续弱点。游戏时刻基准为引导未来研究走向更具时间感知能力的对话式AI提供了基础。演示和数据集可在我们的项目网站https://ga642381.github.io/Game-Time上获取。