autoresearch

To automate an end-to-end AI research lifecycle, use an autonomous orchestration framework that manages literature surveys, hypothesis formulation, experiment execution, and synthesis through continuous inner-outer loop cycles.

A two-loop cycle in autonomous research experimentation is a continuous mechanism pairing an inner loop for experiment execution with an outer loop for synthesis and hypothesis management, sustained by a wall-clock timer to drive progress.

To set up a workspace for autonomous literature surveys and hypothesis management, initialize a structured directory containing research-state.yaml, findings.md, and a literature/ folder to track running memory and progress.

Yes, you can route domain-specific research tasks to specialized skills automatically by implementing a routing mechanism within the orchestration framework that dispatches tasks to appropriate domain skills for execution.

No, autonomous research orchestration does not require constant human input to generate progress reports; it continuously produces findings, presentations, and updates the running memory state across sessions without manual intervention.

Limitations of using autonomous orchestration for AI research projects include the strict dependency on a structured workspace containing research-state.yaml and findings.md, plus the need for a mandatory wall-clock loop to sustain continuous progress.