/paper-writing-bench

name: paper-writing-bench
description: Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".

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