Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

The paper introduces SMART, a symbolic performance‑modeling library for machine‑learning systems that relies almost entirely on natural‑language design documents rather than code. By using AI coding agents to regenerate the implementation from these documents, the framework eliminates the need for continuous refactoring as models and systems evolve. The authors demonstrate that regenerated implementations match hand‑audited reference models to round‑off precision, suggesting that design documents can serve as the durable artifact for ML‑systems co‑design tools.

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