SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
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 ...
| Authors | Rishi Desai, Jesse Hu, Joan Cabezas et al. |
|---|---|
| Primary category | cs.SE |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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