research-pipeline

An end-to-end research pipeline automates idea discovery, implementation, experiment execution, and iterative review loops, outputting structured artifacts like IDEA_REPORT.md for direct publication handoff.

The best way to manage GPU-backed experiment automation is through a pipeline that handles routing between queue-based and direct experiment deployment, ensuring deterministic stage artifacts for reproducible research.

Yes, you can add a human checkpoint after idea discovery by setting AUTO_PROCEED to false in your YAML configuration, allowing manual review before the pipeline continues to implementation and experimentation.

The automated multi-round review loop evaluates experiment outcomes and generates review-stage/AUTO_REVIEW.md artifacts, iteratively refining research results until publication quality is achieved.

Yes, you need YAML configuration to control pipeline gating parameters such as auto-proceed, review difficulty, and auto-write settings, which manage the flow between autonomous experimentation and paper writing.

The research lifecycle produces structured stage artifacts including IDEA_REPORT.md, NARRATIVE_REPORT.md, and AUTO_REVIEW.md, which provide deterministic outputs to move directly into paper writing and iterative drafting.