Realtime-Bench：前沿模型实时交互任务表现不佳
原标题：Import AI 479: Spacewar; low-latency tasks versus LLMs; Genie 4
AI 摘要
得克萨斯大学奥斯汀分校、俄亥俄州立大学、伊利诺伊大学厄巴纳-香槟分校、NVIDIA、西北大学和Steadyworks的研究者构建了Realtime-Bench，包含50个交互式游戏与网页任务，用于评估AI系统在低延迟实时场景下的表现。测试显示，GPT-6 Astra在真实时钟条件下通过率最高，仅为36%，而在暂停时钟条件下高推理模型可达70%至80%。该基准表明，当前AI智能体在需要实时决策的任务上仍存在明显短板。
正文节选
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. LLMs are very bad at low-latency tasks: …Realtime-Bench says it’ll be a while till AI systems can play games or do online auctions for you… Researchers have built Realtime-Bench, “a suite of carefully curated dynamic tasks where delayed actions directly affect success”. The test helps evaluate how well modern, general-purpose AI s