PaperWritingBench (§3)

Faithful implementation of the PaperWritingBench dataset construction procedure from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §3 and App. C, F.2).

The original benchmark contains 200 papers (100 CVPR 2025 + 100 ICLR 2025). For each paper, the authors reverse-engineer the (I, E) tuple by stripping narrative flow from the original PDF using the three prompts in App. F.2. You can use this skill to reverse-engineer your own benchmark cases from any paper PDF.

What this skill does

Given an existing AI research paper (PDF or markdown extract), produce:

• idea.md (Sparse variant), high-level concept note, no math, no experimental results
• idea.md (Dense variant), detailed technical proposal with LaTeX equations and variable definitions, but still no experimental results
• experimental_log.md, exhaustive raw experimental setup, numeric data, and qualitative observations, with all narrative references stripped

These three files form a complete (I, E) input pair for the paper-orchestra pipeline. You can then run the pipeline and compare its output to the original paper using paper-autoraters.

Inputs

• A paper PDF or extracted markdown text. The paper uses MinerU (Wang et al., 2024) for PDF→markdown extraction; you (the host agent) should use whatever PDF extractor your environment provides.
• For controlled experiments, you may also extract figures separately (PDFFigures 2.0 in the paper).

Outputs

• bench/<paper_id>/idea_sparse.md, Sparse variant
• bench/<paper_id>/idea_dense.md, Dense variant
• bench/<paper_id>/experimental_log.md, Experimental log

Workflow

For each paper, run three independent LLM calls using the verbatim prompts below:

1. Sparse idea generation

Load references/sparse-idea-prompt.md. Pass the paper text (or markdown extract) as {paper_content}. The prompt instructs the model to: