arXiv:2606.29957v1 Announce Type: cross Abstract: Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assistance is interactive, with users clarifying goals, adding constraints, and correcting mistakes over multiple turns. We introduce SWE-Together, a multi-turn benchmark reconstructed from real user-agent coding sessions. To make real interactions verifiable, we curate 109 repository-level tasks from 11,260 recorded sessions, selecting sessions with recoverable repository states, clear user goals, an
The increased sophistication of large language models is driving a critical need for more realistic and interactive evaluation benchmarks to truly measure AI coding agent capabilities.
This development addresses a key limitation in AI agent deployment by providing a more reliable way to assess their performance in real-world, interactive coding scenarios, moving beyond static benchmarks.
The shift to interactive, multi-turn benchmarks like SWE-Together means that future AI coding agents will be developed and optimized for adaptability and user collaboration, rather than singular task completion.
- · AI agent developers
- · Software engineering teams
- · Companies adopting AI for software development
- · Developers relying solely on static benchmarks
- · AI agents poorly designed for interactive environments
Improved, more robust AI coding agents will enter the market, capable of more complex and iterative development tasks.
The efficiency and quality of software development could significantly increase, accelerating product cycles and innovation across industries.
The role of human software engineers may evolve further towards oversight, high-level architecture, and complex problem-solving, as agents handle more granular coding tasks.
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Read at arXiv cs.AI