大模型
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Φ-Bench：大语言模型能否工程化支撑自身运行的基础设施？
论文提出 Φ-Bench，用于系统评估 LLM 在 LLM 基础设施栈工程上的能力。现有基准多聚焦孤立 kernel、预定义算子或预设优化目标，难以衡量开放式、长程的基础设施工程能力。Φ-Bench 取材于前沿研究中的优化问题并基于真实代码仓库，覆盖从局部 kernel 级函数补全到长程实现与端到端系统优化等不同复杂度任务。摘要称对前沿 LLM 的实验揭示了其当前能力与局限，但未给出具体分数。
AI推荐理由 arXiv 新提交论文，提出 Φ-Bench 基准，针对现有评测多局限于孤立 kernel、预定义算子或指定优化目标的问题，覆盖 LLM 基础设施栈的开放式长程工程任务，从局部 kernel 函数补全到端到端系统优化，题目源自前沿研究与真实代码仓库。对评估 LLM 自主优化 AI 基础设施能力有实际参考价值；摘要未披露具体模型得分与任务规模，结论需看正文。
78
正文
Computer Science > Computation and Language
arXiv:2609.10226 (cs)
[Submitted on 9 Sep 2026]
Title:$Φ$-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?
Authors:Leilei Ding, Shumin Wang, Yuting Huang, Fanqi Wan, Yinmin Zhang, Qi Han, Yiming Xu, Feiyuan Zhang, Xiaomeng Chu, Guoliang You, Wuyang Zhang, Daxin Jiang, Yanyong Zhang
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Abstract:Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. However, existing benchmarks mainly focus on isolated kernels, predefined operators, or pre-specified optimization targets, and therefore fail to evaluate the ability of LLMs to perform open-ended, long-horizon LLM infrastructure engineering. To address this gap, we present $\Phi$-Bench, a benchmark for systematically evaluating LLMs on engineering the LLM infrastructure stack. Derived from optimization problems studied in frontier research and grounded in real-world code repositories, $\Phi$-Bench provides broad coverage of the LLM infrastructure stack and spans tasks of varying complexity, ranging from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization. Extensive experiments on frontier LLMs reveal their current capabilities and limitations in engineering complex LLM infrastructure, offering insights into the challenges that remain on the path toward autonomous optimization of future AI infrastructure.
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Computation and Language (cs.CL)
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arXiv:2609.10226 [cs.CL]
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arXiv:2609.10226v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.10226
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From: Yuting Huang [view email]
[v1]
Wed, 9 Sep 2026 14:23:47 UTC (2,390 KB)
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