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//idea_sparse.md — Sparse variant
• bench//idea_dense.md — Dense variant
• bench//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.

1. 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.

1. Run the pipeline.
2. 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: woodfishhhh
• Source: woodfishhhh/EZmath_model
• License: MIT

Install and usage instructions live in the source repository linked above.