// 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.