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NewsTopic · 实时交互基准测试Material2 min readDisconfirmAI Editorial

Real-Time Interaction Benchmark: Frontier Models Achieve Only 36% Pass Rate Under Time Pressure

Realtime-Bench tests the real-time decision-making capabilities of frontier large language models using 50 dynamic tasks. Under Wall Clock conditions simulating real-world inference latency, the best pass rate is only 36%; after removing delay (Paused Clock), high-reasoning models reach success rates of 70% to 80%. The paper notes that increased reasoning effort yields mixed results under time pressure, reflecting a trade-off between decision quality and timely execution.

2026-11-02 22:30 GMT+8

Jack Clark, author of the Import AI newsletter, introduced Realtime-Bench in his latest issue—a benchmark suite specifically designed to test the real-time interaction capabilities of large language models. Developed by researchers from the University of Texas at Austin, Ohio State University, the University of Illinois at Urbana-Champaign, NVIDIA, Northwestern University, and Steadyworks, the benchmark comprises 50 dynamic tasks: 30 web-based tasks (covering travel, e-commerce, services, and security) and 20 game tasks (arcade, puzzle, strategy, and simulation). A common feature across all tasks is that delayed actions directly lead to failure—for example, even if reasoning is correct, it is futile once a ticket purchasing window has closed.

The core findings stem from a comparison of three clock conditions. The Wall Clock condition advances the environment based on actual inference latency, simulating real-world deployment scenarios; the Paused Clock halts all delays, providing an ideal upper bound for model capability; and the Token Clock advances simulated time based on the number of generated tokens, isolating physical service speed from the model's temporal perception. Under Wall Clock conditions, OpenAI’s GPT-6 Astra (in high-reasoning mode) achieved the highest pass rate of 36%. In contrast, under Paused Clock conditions, high-reasoning models approached success rates of nearly 70% to 80%. The arXiv paper abstract further distinguishes two metrics: the best success rate under Wall Clock is 44%, while the best Pass^3 rate is 36%.

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The practical implication of this gap is that time constraints themselves constitute a distinct mode of failure. The paper’s authors found that higher reasoning effort yields inconsistent results under time pressure, reflecting a trade-off between decision quality and timely execution—models must simultaneously track changes in world state (RT-Perceive), adjust reasoning depth according to remaining time (RT-Deliberate), and appropriately select action timing (RT-Act). Jack Clark noted that this benchmark serves as an effective proxy indicator for where AI agents will fail; low-scoring tasks represent work that cannot yet be expected of AI agents. As a preprint, the benchmark covers a limited scope of 50 tasks, and specific figures may vary as inference infrastructure accelerates.

Sources

  • importai.substack.com↗
  • arXiv.org↗
Topic

实时交互基准测试

Realtime-Bench是一项由德克萨斯大学奥斯汀分校、英伟达等机构联合推出的基准测试,旨在评估前沿大模型在严格时间约束下的实时交互能力。该测试包含50个动态任务,涵盖网页操作与游戏场景。结果显示,在模拟真实推理延迟的“墙钟”条件下,表…

3 steps1 piecesUpdated 2026-11-03 05:02

How it got here

  1. 2026-10-15Realtime-Bench论文v1版本提交至arXiv,正式提出针对实时交互问题的评估框架。arxiv.org ↗
  2. 2026-10-22论文更新至v3版本,明确在Wall Clock条件下最佳Pass^3仅为36%,并引入Token Clock以分离服务延迟与模型能力。arxiv.org ↗
  3. 2026-11-02Import AI通讯发布第479期,详细解读Realtime-Bench结果,指出低延迟任务是大模型的短板,并列举了参与测试的具体模型如GPT-6 Astra和Claude Fable 5.1。importai.substack.com ↗

What is still unsettled

  • 如何通过自适应推理(adaptive reasoning)在不牺牲决策质量的前提下提升实时响应速度?
  • 除了游戏和网页任务,Realtime-Bench的方法论是否适用于金融交易或自动驾驶等更高风险的实时场景?
  • 不同架构的模型(如专门优化的实时模型 vs 通用大模型)在Token Clock条件下的表现差异有多大?
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