// 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://t.co/l9Uv8JZ4GX