Autoresearch proposes packaging ML runs as studies with questions, analysis, and code diffs
morgymcg · x · 2026-07-21
The post argues that autoresearch raises ML work to a higher abstraction level: instead of isolated training runs, researchers should package runs as studies containing the question, analysis, decision, and code diffs.
- Researchers already think this way informally, but documentation quality varies.
- Agents need a data structure that is easy to write and easy to read.
- The point is to make ML research workflows more machine-readable and more reusable.
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