FastCode: Fast and Cost-Efficient Code Understanding and Reasoning

• URL: http://arxiv.org/abs/2603.01012v2
• Date: Tue, 03 Mar 2026 10:18:10 GMT
• 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,
• Abstract summary: Repository-scale code reasoning is a cornerstone of modern AI-assisted software engineering.<n>FastCode is a framework that decouples repository exploration from content consumption.
• Score: 32.264145740214616
• License: http://creativecommons.org/licenses/by/4.0/
• 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 FastCode, a framework that decouples repository exploration from content consumption. FastCode 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 FastCode consistently outperforms state-of-the-art baselines in reasoning accuracy while significantly reducing token consumption, validating the efficiency of scouting-first strategies for large-scale code reasoning. Source code is available at https://github.com/HKUDS/FastCode.

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