aras-research-planner

To generate a structured research plan from a topic prompt, you provide a topic string to extract a strict JSON object containing defined hypotheses, experiments, metrics, and an outline. This automated research planning approach ensures clear hypothesis validation and testable experimental design.

A strict JSON research plan includes keys for hypothesis, questions, experiments, metrics, outline, section_briefs, keywords, and domain. These fields provide detailed technical briefs for each paper section, covering equations, algorithms, and ablation study specifics to guide research cycles.

Yes, automated research planning works for computer science systems by generating technically grounded section briefs. It outputs a JSON plan detailing architectural details, algorithms, and specific metrics required for rigorous hypothesis validation and ablation studies in computational studies.

Yes, the research planner supports ablation studies and manipulation checks by including them within the generated experiments and metrics fields. This ensures the final JSON plan provides an end-to-end workflow from topic input to a fully testable experimental design.

The best way to outline a research paper using a JSON plan is to follow the generated outline and section_briefs fields. These provide technically grounded briefs for each section, ensuring your research planning includes detailed equations, algorithms, and experimental design specifics.

You define metrics and hypotheses for automated research planning by inputting your topic string, which the system processes to output a strict JSON object. This object explicitly separates your testable hypothesis, experimental questions, and validation metrics into dedicated JSON keys.