# Google DeepMind 与 MIT 等发布 SMART 论文：设计文档取代代码作为 ML 性能建模库的核心产物

- 来源：elvis (@omarsar0)
- 发布时间：2026-09-07 23:25
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmtreqaiq01u0ro8dglk70p6c
- 原文链接：https://x.com/omarsar0/status/2096983084956852537

## AI 摘要

Google DeepMind、MIT、Stanford 等机构提出 SMART，一个 ML 性能建模库，其 main 分支几乎不含代码，仓库是自然语言设计文档的有向图，编码子智能体在版本更新时从文档重新生成全部实现。

## 正文

// Design Docs Are All You Need //

Banger paper from Google DeepMind, MIT, and colleagues.

What a genuinely strange and interesting paper this one is.

Here is the setup:

They maintain a performance-modeling library whose main branch contains almost no code.

The repository is a directed graph of natural-language design docs. Coding sub-agents regenerate the entire implementation from those docs whenever a version updates.

Every human change is an edit to a doc.

The premise is that ML performance modeling invalidates its own abstractions every hardware and model generation, and coding agents are now cheap enough that regenerating a library beats patching one.

Two things make the regeneration reliable. The design docs are written around step-by-step worked examples, which act as in-context demonstrations for the generating agents. The system is also anchored on a minimal recursively defined operator IR with symbolic cost expressions in SymPy.

Regenerated implementations reproduce hand-audited reference models to round-off precision, including DeepSeek-V3 serving on a TPU pod slice.

Paper: https://academy.dair.ai/papers/design-docs-are-all-you-need-an-ai-native-machine-learning-performance-tool-2609.05364