[2608.00280] Benchmarking LLMs on File System Design and Implementation

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arXiv:2608.00280 (cs)

[Submitted on 31 Jul 2026 (v1), last revised 4 Aug 2026 (this version, v2)]

Title:Benchmarking LLMs on File System Design and Implementation

Authors:Yuqi Xue, Daixuan Li, Jian Huang<br>View a PDF of the paper titled Benchmarking LLMs on File System Design and Implementation, by Yuqi Xue and 2 other authors

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Abstract:Large Language Models (LLMs) are fundamentally transforming computer system research and development. As we employ LLMs in file system (fs) development, it is essential to understand their capabilities, limitations, and operational efficiency for domain-specific tasks. We present \phi-Bench, an LLM benchmarking framework for fs-specific tasks. To facilitate benchmarking, we develop six types of tasks in \phi-Bench: basic understanding, basic implementation, performance modeling, debugging, optimization, and new feature development. Each type emphasizes different LLM capabilities: instruction following, knowledge recall, reasoning, or coding. To create high-quality tasks while achieving broad coverage with minimal human effort, we develop a new AI-assisted task generation pipeline in addition to expert-written and textbook-adapted tasks. With 505 tasks in \phi-Bench, we conduct an empirical study with both open source (DeepSeek-V4-Flash, GLM-5.1, and MiniMax-M2.7) and proprietary (Claude-Opus-4.7, GPT-5.2, and Gemini-3.1-Pro) LLMs. Our study discloses the model efficiency for different tasks, causes of failed fs tasks, and techniques for mitigating LLM failures. We will open source \phi-Bench to facilitate public research on using LLMs for fs development.

Subjects:

Operating Systems (cs.OS); Software Engineering (cs.SE)

Cite as:<br>arXiv:2608.00280 [cs.OS]

(or<br>arXiv:2608.00280v2 [cs.OS] for this version)

https://doi.org/10.48550/arXiv.2608.00280

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arXiv-issued DOI via DataCite

Submission history<br>From: Daixuan Li [view email]<br>[v1]<br>Fri, 31 Jul 2026 20:31:32 UTC (1,195 KB)

[v2]<br>Tue, 4 Aug 2026 05:00:56 UTC (1,195 KB)

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