// 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: