设计文档至上：AI原生ML性能工具
Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
MIT团队推出SMART工具，用设计文档替代代码库，AI代理根据文档自动生成实现，精确复现DeepSeek-V3性能。
研究人员提出SMART工具，这是一个基于符号的ML系统性能建模库。该库主分支几乎不包含代码，而是由自然语言设计文档组成。SMART通过逐步工作示例和最小化操作符IR实现可靠再生，能精确复现DeepSeek-V3在TPU pod上的服务性能。研究表明设计文档而非代码可作为ML系统协同设计工具的持久性产物。
Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models and systems, forcing perpetual refactoring of performance-modeling frameworks. Meanwhile, AI coding agents have become fast and capable enough that regenerating an entire library is cheaper than paying down the tech debt of incrementally patching it. We describe SMART, a rigorous symbolic performance-modeling library for ML systems whose main branch contains almost no code: the repository is a DAG of self-contained natural-language design docs, coding sub-agents regenerate the implementation from only the docs on new version updates, and every human change is a natural-language edit to a doc--self-documenting by construction. Two ingredients make regeneration reliable: (i) a design-doc style built around step-by-step worked examples that act as in-context demonstrations for the generating agents, and (ii) a minimal, recursively defined operator IR with symbolic (SymPy) cost expressions, a fast analytical roll-up mode for large sweeps, and a slow modulo-scheduling mode for fine-grained schedule studies. Regenerated implementations reproduce hand-audited reference models--including DeepSeek-V3 serving on a TPU pod slice--to round-off precision, suggesting that design docs--not code--can be the durable artifact for ML-systems co-design tools.
