AI跑长任务：平均花85分钟，成功率仅4%
AI能帮你写邮件、改代码，但让它独立完成一个需要几小时、几百步的复杂任务，结果惨不忍睹。新基准测试Long-Horizon-Terminal-Bench设计了46个长周期任务，包括复现实验、软件工程、多模态分析等，每个任务分解成细粒度子任务，给中间分。15个前沿模型平均每任务消耗990万token、跑231轮、耗时85分钟，最强模型在95%得分阈值下通过率仅15.2%，完美通过率10.9%，所有模型平均通过率4.3%和1.7%。这不是你明天能用上的工具，但它告诉你：AI在需要长期规划和迭代调试的任务上，离靠谱还差得远。
📄 原文摘要(英文)
AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks intermediate progress and partial solutions, yielding sparse reward signals and an incomplete picture of agent capability. We introduce Long-Horizon-Terminal-Bench, a terminal benchmark of 46 long-horizon tasks spanning nine categories, including experiment reproduction, software engineering, multimodal analysis, interactive games, and scientific computing. Each task follows a Terminal-Bench-style setup with a reference solution or simulation engine, but is further decomposed into fine-grained graded subtasks. This design enables dense intermediate rewards and partial credit, allowing evaluation to capture not only whether an agent reaches the final goal, but also how far it progresses on open-ended workflows. Tasks in Long-Horizon-Terminal-Bench typically require hundreds of episodes and minutes to hours of execution, stressing long-horizon planning, long-context management, and iterative debugging rather than one-shot problem solving. We evaluate 15 frontier models and find that agents consume on average 9.9M tokens per task, with roughly 231 episodes and 85.3 minutes of execution time per run, making Long-Horizon-Terminal-Bench more demanding than prior terminal-based benchmarks. Even the strongest tested model achieves 15.2% pass@1 at a partial-reward threshold of 0.95 and 10.9% at a perfect-reward threshold of 1.0, while the mean pass rate across models is 4.3% and 1.7% under the two thresholds, respectively. These results reveal headroom for improvement. We further analyze failure modes and error patterns, and release Long-Horizon-Terminal-Bench to support future progress on long-horizon terminal agents.