[2603.01012] FastCode: Fast and Cost-Efficient Code Understanding and Reasoning

Computer Science > Software Engineering arXiv:2603.01012 (cs) [Submitted on 1 Mar 2026] Title:FastCode: Fast and Cost-Efficient Code Understanding and Reasoning Authors:Zhonghang Li, Zongwei Li, Yuxuan Chen, Han Shi, Jiawei Li, Jierun Chen, Haoli Bai, Chao Huang View a PDF of the paper titled FastCode: Fast and Cost-Efficient Code Understanding and Reasoning, by Zhonghang Li and 7 other authors View PDF HTML (experimental) Abstract: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 existing agentic approaches often waste computational resources through inefficient, iterative full-text exploration. To address this, we introduce \model, a framework that decouples repository exploration from content consumption. \model\ utilizes 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 the overhead of full-text ingestion. By leveraging structure-aware navigation tools regulated by a cost-aware policy, the framework constructs high-value contexts in a single, optimized step. Extensive evaluations on the SWE-QA, LongCodeQA, LOC-BENCH, and GitTaskBench benchmarks demonstrate that \model\ consistently outperforms state-of-the...