CLEAR: Structured Specifications for Controllable Long-Horizon ML Research Engineering
- Published
- Source
- arXiv
- Paper number
- 1427
- Field
- Software Engineering
- arXiv ID
- 2611.06263
Key points
- The authors identified that research artifacts are not executable specifications as the root cause of cascading errors.
- CLEAR transforms heterogeneous research artifacts into structured and human verifiable execution specifications before generating executable code.
- Specification-conditioned generation and repair on localized units avoids repeated end-to-end regeneration.
- On MLE-Bench Lite, CLEAR achieves 77.27% Any Medal while substantially reducing repair iterations on representative tasks.
- Compared with AiScientist, it reduces cost by 35.16% while retaining competitive performance.
- One analysis reports a higher average score while reducing the average cost by 85.6%.
Paper links
External research summaries. These are not HDATF publications or measured product results.