AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments. Yet current agent benchmarks largely evaluate short-form tasks, such as single pull requests, small tickets, or 5-10 minute exercises, limiting our ability to measure agents' capabilities in planning, long-context understanding, and memory use. We introduce SWE-Marathon, a benchmark of 20 long-horizon tasks spanning software engineering and adjacent technical domains. Each task consists of a unique executable environment, a human-written reference solution, and a multi-layer verification suite. Logged agent attempts average 27.2M total tokens, making SWE-Marathon substantially longer-horizon than existing SWE and command-line agent benchmarks. Current frontier coding agents solve fewer than 30% of tasks. Failures often arise from poor self-verification, self-reported infeasibility, and premature termination. We also observe reward-hacking behavior in 13.8% of rollouts, where agents attempt to exploit the environment or verifier to bypass the intended workflow. SWE-Marathon includes adversarial review of test suites and execution environments, as well as multi-layer checks designed to prevent shortcut solutions. We release SWE-Marathon, evaluation code, and agent trajectories at https://swe-marathon.org/.

翻译：AI智能体日益被期望能够完成需要数小时、数百万token密集进展及复杂环境的长期工作流。然而，当前的智能体基准主要评估短时任务，例如单次代码合并请求、小型工单或5-10分钟的练习，这限制了我们在规划、长上下文理解和记忆使用方面衡量智能体能力的能力。我们提出了SWE-Marathon，一个包含20个跨越软件工程及相关技术领域的长期视界任务的基准。每个任务包含一个独特的可执行环境、一份人工撰写的参考解决方案以及一套多层验证体系。记录的智能体尝试平均消耗2720万个token，使SWE-Marathon的视界长度显著超过现有的SWE和命令行智能体基准。当前前沿的编码智能体解决的任务比例不足30%。失败通常源于自我验证能力不足、智能体自报告不可行性以及过早终止。我们还在13.8%的执行轮次中观察到奖励黑客行为，即智能体试图利用环境或验证器绕过既定工作流。SWE-Marathon包含对测试套件和执行环境的对抗性审查，以及旨在防止捷径解决方案的多层检查。我们在https://swe-marathon.org/上开源了SWE-Marathon、评估代码及智能体轨迹。