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

TL;DR - SMART is an ML performance-modeling library represented primarily as a DAG of natural-language design documents, with coding agents regenerating its implementation for each version. The approach suggests design specifications may be more durable than source code for fast-changing ML systems.

• Worked examples in the design documents serve as in-context demonstrations that improve regeneration reliability.
• A minimal recursive operator IR combines symbolic SymPy cost expressions with analytical roll-up and modulo-scheduling modes.
• Generated implementations matched hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.
• Human changes are made to the design documents rather than implementation code, making the system self-documenting by construction.