ar9av / paper-writing-bench

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:

• Stop extracting at empirical verification (no Experiments / Results / Comparisons)
• Use first-person future tense ("We propose to explore...")
• Avoid LaTeX math; describe components by function
• Anonymize authors and titles

Output: idea_sparse.md with the four sections (Problem Statement, Core
Hypothesis, Proposed Methodology high-level, Expected Contribution).

2. Dense idea generation

Load references/dense-idea-prompt.md. Same input. The prompt instructs
the model to:

• Preserve mathematical formulations using LaTeX
• Define every variable used in equations
• Include specific architectural choices and dimensions
• Same exclusion zone (no experiments)

Output: idea_dense.md with the four sections (Problem Statement, Core
Hypothesis, Proposed Methodology detailed, Expected Contribution).

3. Experimental log generation

Load references/experimental-log-prompt.md. Same input. The prompt
instructs the model to:

• Use past-tense persona ("We ran...", "The results were...")
• Strip all references to figure/table numbers
• Deconstruct tables into raw numeric data
• Log figure findings as factual observations
• Anonymize authors

Output: experimental_log.md with sections for Setup, Raw Numeric Data,
and Qualitative Observations.

Critical rules from the prompts

These are excerpted from App. F.2. The host agent MUST honor them:

• No citations. None of the three outputs may contain \cite,
  reference numbers, or author names from the source paper.

• No URLs. Strip all hyperlinks.

• Anonymize. Author identities, affiliations, acknowledgements all
  removed.

• Self-contained. Each file must make sense without the original paper.

• No experimental leakage in idea files. The Sparse and Dense ideas
  must stop where empirical verification begins. They describe what will
  be done, not what was done.

• No table/figure references in experimental log. No "as shown in
  Table 1", "see Fig. 5". The downstream paper-orchestra pipeline will
  generate its own figures and tables — the log must not assume any
  particular ones exist.

• 100% numeric accuracy in experimental log. This becomes the ground
  truth for the section-writing-agent and content-refinement-agent's
  hallucination check.

How the bench is used

After producing (idea_sparse.md, idea_dense.md, experimental_log.md) for
a paper:

1. Pick a variant (Sparse or Dense) — the paper ablates both, with Dense
   producing more rigorous methodology and Sparse exercising the system's
   robustness on under-specified inputs.

2. Drop the chosen idea.md, plus experimental_log.md, plus a
   template.tex for the target conference, plus a
   conference_guidelines.md, into a paper-orchestra workspace.

3. Run the pipeline.

4. Compare the generated paper against the original using
   paper-autoraters (citation F1, lit review quality, SxS paper quality).

Resources

• references/bench-overview.md — the 200-paper bench, venue cutoffs, sizes
• references/sparse-idea-prompt.md — verbatim from App. F.2
• references/dense-idea-prompt.md — verbatim from App. F.2
• references/experimental-log-prompt.md — verbatim from App. F.2