arXiv:2606.07682v1 Announce Type: cross Abstract: 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
The rapid advancement in AI agent capabilities necessitates better evaluation benchmarks to understand their true potential and limitations for complex, long-duration tasks.
This benchmark addresses a critical gap in assessing AI agents' ability to handle real-world, multi-step software engineering challenges, which is crucial for their broader adoption and impact on white-collar work.
The introduction of SWE-Marathon shifts the focus of AI agent evaluation from short-form tasks to complex, long-horizon workflows, providing a more realistic measure of agentic intelligence and generalizability.
- · AI agent developers
- · Companies investing in AI automation
- · Open-source software communities
- · Human software developers (routine tasks)
- · Consulting firms (process automation)
- · Companies relying on short-term AI evaluations
Improved benchmarks will accelerate the development of more capable and robust AI agents for software engineering.
Enterprise software development workflows will be increasingly automated, leading to significant changes in team structures and product development cycles.
The definition of human 'coding' work could transform from direct implementation to high-level architectural design, oversight, and complex problem-solving outside agent capabilities.
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Read at arXiv cs.AI