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.

翻译：高级智能体正日益展现出作为自主工程师的潜力，这催生了对能够捕捉现实世界开发复杂性的评估基准的需求。此类环境通常同时涉及复杂代码和大规模数据（例如文件系统）。然而，现有基准往往孤立地评估以代码为中心或以数据为中心的能力，与现实开发场景之间存在明显差距。本文通过引入CODA-BENCH弥合了这一差距，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，智能体必须在此环境中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包含涵盖31个社区的1,009个任务，每个任务环境平均包含980个文件，模拟了真实的数据规模和噪声。对高级智能体的评估表明，即使是最优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数据密集型任务中能力的显著不足，并为未来研究指明了有前景的方向。