research-pipeline

An automated research pipeline runs end-to-end from idea discovery and literature review to GPU experiment execution and iterative auto-review. It produces deterministic outputs like IDEA_REPORT.md and AUTO_REVIEW.md to yield a submission-ready paper.

An auto review loop executes iterative review-and-fix cycles on your research output, generating an AUTO_REVIEW.md file. It performs up to four rounds of automated refinement and produces a final assessment to improve paper quality.

After idea discovery generates a ranked shortlist in IDEA_REPORT.md, the pipeline bridges to code implementation and deploys GPU experiments. It monitors the execution to produce full-scale experimental results for your chosen idea.

Yes, the autonomous research lifecycle workflow supports optional arxiv metadata retrieval. This feature enriches the initial literature review and idea validation stages before moving into implementation and GPU experiment execution.

The workflow uses staged orchestration with controlled gating, supporting both AUTO_PROCEED and HUMAN_CHECKPOINT modes. This ensures safe failure handling across stages while allowing you to decide the level of human oversight.

Limitations include the constraint of up to four auto-review rounds and reliance on staged orchestration for safe failure handling. The pipeline requires controlled gating to proceed correctly, meaning unhandled failures across stages may halt the workflow.