AI 干活只学短动作，一遇长任务就断；这篇专治这个
现在的 AI 智能体学的是「短动作」:点一下、调一次接口、跑一条命令。但真实工作不是短动作，是几小时甚至几天的长跑——先探索、再判断、根据反馈改策略、最后交付。问题在于，训练数据大多是环境自动生成的，关键步骤被提前简化掉了，AI 根本没机会学「怎么自己摸索」。这篇论文做了一个叫 TerminalHorizon 的自动数据合成引擎：从真实专业工作出发，先让 AI 独立跑一遍，找出哪些关键工作被偷懒省掉了，再据此重建环境，逼着 AI 重新探索、试错、迭代。用这套数据微调出的模型，在 12 个基准上全面超过原版，包括写代码、真实生产环境任务和通用推理；在 12 小时的研究任务里，它也会在初版方案之后继续实验、根据反馈改进、把有效成果保留进最终交付。它不是你明天就能装上的工具，但它指向一个更本质的问题：AI 要真正干活，缺的不是更快的指令执行，而是「扛得住长任务」的经验。
📄 原文摘要(英文)
Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool calls, but they do not fully show how agents accumulate evidence through exploration, use it to inform judgments, continually adjust strategies based on feedback, and turn intermediate results into final deliverables. High-quality long-horizon trajectories capture these agentic behavioral patterns and their connections within a single problem-solving process, providing richer experience for sustained autonomous work. However, environment synthesis may complete or simplify the key work underlying these patterns in advance and thus remove the need for exploration, implementation, and verification that the agent was meant to perform. To address this, we introduce TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents. Starting from real-world professional work, it uses independent execution to identify key work omitted during construction and uses the resulting evidence to guide environment reconstruction. To extend these patterns across stages, it progressively expands tasks under the same goal, introducing new requirements that build on prior results and call for further exploration and improvement. Using this engine, we construct 1.5K task environments and perform supervised fine-tuning of Qwen3.5-27B on the resulting dataset to obtain TerminalHorizon-27B. We achieve substantial performance gains on all 12 benchmarks spanning coding-agent tasks, agentic tasks in real-world production settings, and general reasoning. Multiple RSI case studies, each with a 12-hour research budget, further demonstrate the model's ability to continue experimenting beyond an initial solution, improve its methods based on feedback, and retain effective improvements in its final deliverables.