CSBench: A Comprehensive Benchmark for Evaluating Project-Level System Construction in Computer Science

While LLM-based coding agents have advanced to repository-scale engineering, existing benchmarks predominantly focus on software maintenance, overlooking the foundational capability of system construction. We introduce CSBench, a benchmark evaluating project-level Spec-to-Code implementation across core computer science domains (e.g., OS, compilers, databases). CSBench features 100 expert-curated tasks from top-tier university assignments within containerized environments. Evaluating 12 state-of-the-art LLMs reveals a stark performance disparity: even top-tier models like GPT-5.2 see success rates plummet from over 90% in algorithmic tasks to below 35% in system-level domains. Our analysis identifies a fundamental deficiency in systemic reasoning and construction knowledge as the primary bottleneck, where agents either terminate prematurely due to overconfidence or fail to translate execution feedback into valid architectural fixes, highlighting CSBench as a necessary tool to bridge this gap and advance autonomous agents toward deep computer science mastery.