FastCode: Fast and Cost-Efficient Code Understanding and Reasoning

Repository-scale code reasoning is a cornerstone of modern AI-assisted software engineering, enabling Large Language Models (LLMs) to handle complex workflows from program comprehension to complex debugging. However, balancing accuracy with context cost remains a significant bottleneck, as coding agents repeatedly process full code accumulated in their exploration history. To address this, we introduce FastCode, a framework that decouples repository exploration from content consumption. FastCode employs a structural scouting mechanism to navigate a lightweight semantic-structural map of the codebase, allowing the system to trace dependencies and pinpoint relevant targets without loading code bodies into the exploration context. By integrating structure-aware navigation with cost-aware context selection, the framework constructs high-value contexts for final reasoning while reducing repeated token overhead. Extensive evaluations on the SWE-QA, LongCodeQA, GitTaskBench, and LOC-BENCH benchmarks show that FastCode achieves favorable trade-offs between reasoning performance and token efficiency, highlighting its effectiveness for repository-scale code reasoning. Source code is available at https://anonymous.4open.science/r/FastCode.