标题： Trae代理：一种基于大语言模型的软件工程代理，具有测试时缩放功能 显示英文标题

标题： Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling

摘要： 软件问题解决是软件工程中的一个关键挑战，近年来引起了越来越多的关注。随着大型语言模型（LLMs）的快速发展，已经在解决现实世界的软件工程任务方面取得了显著进展。最近的研究引入了集成推理技术，以提高基于LLM的问题解决性能。然而，现有的基于提示的方法在有效探索大型集成空间方面仍然存在局限，并且缺乏仓库级别的理解能力，这两方面都限制了它们的整体效果。在本文中，我们提出了Trae Agent，这是第一个基于代理的集成推理方法，用于仓库级别的问题解决。Trae Agent将我们的目标表述为一个最优解搜索问题，并通过生成、剪枝和选择的模块化代理来解决两个关键挑战，即大型集成空间和仓库级别理解。我们在广泛采用的SWE-bench基准上使用三种领先的LLM进行了广泛的实验，将Trae Agent与四种最先进的集成推理技术进行比较。实验结果表明，Trae Agent在Pass@1指标上相对于所有基线平均提高了10.22%，表现优异。Trae Agent在SWE-bench Verified排行榜上获得了第一名，具有显著的Pass@1得分为75.20%。我们很高兴将Trae Agent作为开源项目发布，以支持研究社区，所有资源均可在https://github.com/bytedance/trae-agent获取。 显示英文摘要

摘要： Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models (LLMs), substantial progress has been made in addressing real-world software engineering tasks. Recent studies have introduced ensemble reasoning techniques to enhance the performance of LLM-based issue resolution. However, existing prompting-based methods still face limitations in effectively exploring large ensemble spaces and lack the capacity for repository-level understanding, both of which constrain their overall effectiveness. In this paper, we propose Trae Agent, the first agent-based ensemble reasoning approach for repository-level issue resolution. Trae Agent formulates our goal as an optimal solution search problem and addresses two key challenges, i.e., large ensemble spaces and repository-level understanding, through modular agents for generation, pruning, and selection. We conduct extensive experiments using three leading LLMs on the widely-adopted SWE-bench benchmark, comparing Trae Agent against four state-of-the-art ensemble reasoning techniques. Experimental results demonstrate that Trae Agent consistently achieves superior performance, with an average improvement of 10.22% over all baselines in terms of Pass@1. Trae Agent has achieved first place on the SWE-bench Verified leaderboard, with a notable Pass@1 score of 75.20%. We are pleased to release Trae Agent as an open-source project to support the research community, with all resources available at https://github.com/bytedance/trae-agent.