# Φ-Bench：大语言模型能否工程化支撑自身运行的基础设施？

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-09 08:00
- AIHOT 分数：39
- AIHOT 链接：https://aihot.news/items/cmtuxuyrr0igfrorpjm1oao4v
- 原文链接：https://arxiv.org/abs/2609.10226

## AI 摘要

研究者提出 Φ-Bench，用于系统评估 LLM 工程化 LLM 基础设施栈的能力，任务源自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 级函数补全到长周期实现与端到端系统优化。对前沿 LLM 的实验揭示了它们在工程化复杂 LLM 基础设施上的现有能力与局限。

## 正文

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 Φ-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, Φ-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.