paper-plan

Generating a structured paper outline from experiment results requires applying a Claims-Evidence Matrix to map narrative reports and JSON data into organized sections, figures, and tables. This process converts raw experiment data into a coherent academic story.

Creating a venue-specific outline for ICLR, NeurIPS, ICML, CVPR, or IEEE journals involves tailoring section ordering, length constraints, and page budgets to match the target venue. This approach aligns the structure with specific academic formatting standards.

Building a Claims-Evidence Matrix for a research paper involves mapping narrative conclusions and auto-review outputs against experiment data. This ensures every claim is supported by JSON results or figures, enforcing a coherent academic narrative within the outline.

Planning paper sections and figures from a narrative report is best done by merging documents like STORY.md with experiment outputs to enforce page-budget constraints. This yields an organized outline with sections, figures, and tables planned.

Automated outline generation works with raw JSON experiment data by merging it with narrative files and auto-review conclusions. This integration enforces a Claims-Evidence structure and outputs a complete, structured outline with planned figures.

Using automated paper outlining instead of manual section planning is ideal when you have narrative reports and experiment data ready. It enforces a Claims-Evidence Matrix and venue-aware structure, saving time on organizing sections, figures, and tables.