Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.
The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.
- Repository main branch holds almost no code; design docs are the durable artifact.
- Worked-example doc style serves as in-context demonstrations for generating agents.
- Operator IR uses symbolic SymPy cost expressions with fast analytical roll-up mode.
- Regenerated models match hand-audited references, including DeepSeek-V3 serving on a TPU pod slice.
- Products
- SMART
- AI models
- DeepSeek-V3
From the original article
1 min read at arxiv.orgMachine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models and systems, forcing perpetual refactoring of performance-modeling frameworks.
By Samuel Kushnir · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.