What problem does it solve?
Running a complete research project requires coordinating many disconnected stages—literature survey, baseline reproduction, ideation, experimentation, analysis, writing, and review—which is slow and error-prone when done manually.
Core Features & Use Cases
- Nine-Stage Pipeline: Chains scout, baseline, idea, experiment, analysis campaign, optimize, write, review, and finalize stages into one workflow.
- Human Checkpoints: Enforces gate reviews after the scout and idea stages so users confirm research direction before autonomous execution continues.
- Evidence-First Writing: Requires every paper claim to trace back to an experiment artifact, with git commits after each stage for durable progress.
- Use Case: A researcher wants to investigate a new method for a machine learning task; the pipeline surveys the literature, reproduces a baseline, generates and tests hypotheses, runs ablations, and produces a reviewed LaTeX paper draft.
Quick Start
Run the full DeepScientist research pipeline on my research topic and pause for my confirmation after the scouting and idea stages.
