CODA-BENCH：代码智能体能否胜任数据密集型任务？

摘要

高级智能体正日益展现出作为自主工程师的运行潜力，这催生了对能够捕捉真实世界开发复杂性的评估基准的迫切需求。此类环境通常涉及复杂代码与大规模数据（即文件系统）。然而，现有基准通常孤立评估以代码为中心或以数据为中心的能力，与实际开发场景存在显著差距。本文通过提出CODA-BENCH来弥合这一差距，这是首个在数据密集型环境中联合评估代码与数据智能的基准。我们基于Kaggle生态系统（包含数百个数据集）构建了一个数据密集型Linux沙箱，其中智能体必须主动探索复杂的文件层次结构，以识别相关资源并为数据驱动的分析任务生成代码。CODA-BENCH包含跨越31个社区的1,009项任务，每个任务环境平均包含980个文件，模拟了真实的数据规模与噪声。对先进智能体的评估显示，即便表现最佳的系统也难以有效整合数据发现与代码执行，其成功率仅为61.1%。这些结果凸显了当前智能体在处理数据密集型任务时的能力短板，并为未来研究指出了有前景的方向。

English

Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development. Such environments typically involve both complex code and large-scale data (i.e., file system). However, existing benchmarks usually evaluate code-centric or data-centric capabilities in isolation, leaving a clear gap with real development scenarios. In this paper, we bridge this gap by introducing CODA-BENCH, the first benchmark to jointly evaluate code and data intelligence in a data-intensive environment. We construct a data-intensive Linux sandbox based on the Kaggle ecosystem (containing hundreds of datasets), where agents must actively explore complex file hierarchies to identify relevant resources and generate code for data-driven analytical tasks. CODA-BENCH comprises 1,009 tasks spanning 31 communities, with each task environment containing an average of 980 files, simulating realistic data scale and noise. Evaluations of advanced agents reveal that even top-performing systems struggle to effectively integrate data discovery with code execution, achieving a success rate of only 61.1%. These results highlight a substantial gap in current agentic capabilities for data-intensive tasks and point to promising directions for future research.