PaperOrchestra

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Bu listing icin henuz AI raporu yok.

SUMMARY

An automated AI research-paper writer based off PaperOrchestra paper's implementation through a skills : five composable skills + benchmark + autoraters using any coding agent (Claude Code, Cursor, Antigravity, Cline, Aider). No API keys, no LLM SDKs.

README.md

paper-orchestra

A pluggable skill pack that lets any coding agent in Claude Code, Cursor,
Antigravity, Cline, Aider, OpenCode, etc. which can run the
PaperOrchestra multi-agent pipeline for
turning unstructured research materials into a submission-ready LaTeX paper.

Song, Y., Song, Y., Pfister, T., Yoon, J.
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing.
arXiv:2604.05018, 2026. https://arxiv.org/pdf/2604.05018

PaperOrchestra paper — first page preview
Click to read the paper on arXiv

Why this exists

The paper defines a five-agent pipeline

  • Outline
  • Plotting
  • Literature Review
  • Section Writing
  • Content Refinement

that substantially outperforms single-agent and tree-search baselines on the PaperWritingBench benchmark (50–68% absolute win margin on literature review quality; 14–38% on overall quality). The paper ships the exact prompts for every agent in Appendix F.

This repo turns those prompts, schemas, halt rules, and verification pipelines into a set of host-agent-executable skills. There are no API keys, no SDK dependencies, no embedded LLM calls. The skills are instruction documents plus deterministic helpers; your coding agent does all LLM reasoning and web search using its own tools.

image

How skills work here

Each skill is:

  • SKILL.md — a dense instruction document the host agent reads and follows.
  • references/ — reference material: verbatim paper prompts (Appendix F), JSON
    schemas, rubrics, halt rules, example outputs.
  • scripts/purely deterministic local helpers: JSON schema validation,
    Levenshtein fuzzy matching, BibTeX formatting, dedup, LaTeX sanity checks,
    coverage gates. No network, no LLM, no API keys.

Everything else (LLM reasoning, web search, Semantic Scholar lookups, LaTeX compilation) is delegated to the host agent by instruction. See skills/paper-orchestra/references/host-integration.md for per-host invocation (Claude Code, Cursor, Antigravity, Cline, Aider).

The seven skills

Skill Paper step # LLM calls Role
paper-orchestra orchestrator Top-level driver. Coordinates the other six.
outline-agent Step 1 1 Idea + log + template + guidelines → structured outline JSON (plotting plan, lit review plan, section plan).
plotting-agent Step 2 ~20–30 Execute plotting plan; render plots & conceptual diagrams; optional VLM-critique refinement loop; caption everything.
literature-review-agent Step 3 ~20–30 Web-search candidates; Semantic Scholar verify (Levenshtein > 70, cutoff, dedup); draft Intro + Related Work with ≥90% citation integration.
section-writing-agent Step 4 1 One single multimodal call: draft remaining sections, build tables from experimental log, splice figures.
content-refinement-agent Step 5 ~5–7 Simulated peer review; accept/revert per strict halt rules; safety constraints prevent gaming the evaluator.
paper-writing-bench §3 Reverse-engineer raw materials (Sparse/Dense idea, experimental log) from an existing paper to build benchmark cases.
paper-autoraters App. F.3 Run the paper's own autoraters: Citation F1 (P0/P1), LitReview quality (6-axis), SxS paper quality, SxS litreview quality.

Steps 2 and 3 run in parallel (see skills/paper-orchestra/references/pipeline.md).

Install

git clone <this repo> ~/paper-orchestra
cd ~/paper-orchestra
pip install -r requirements.txt   # deterministic helpers only

Then symlink the skills you want into your host's skill directory:

# Claude Code
mkdir -p ~/.claude/skills
for s in paper-orchestra outline-agent plotting-agent literature-review-agent \
         section-writing-agent content-refinement-agent paper-writing-bench \
         paper-autoraters; do
  ln -sf ~/paper-orchestra/skills/$s ~/.claude/skills/$s
done

# Or for ~/.all-skills/
mkdir -p ~/.all-skills
for s in paper-orchestra outline-agent plotting-agent literature-review-agent \
         section-writing-agent content-refinement-agent paper-writing-bench \
         paper-autoraters; do
  ln -sf ~/paper-orchestra/skills/$s ~/.all-skills/$s
done

For Cursor / Antigravity / Cline / Aider, see skills/paper-orchestra/references/host-integration.md.

Optional integrations

The pipeline requires zero API keys to run under any host with a native
web search tool. Two optional integrations improve throughput or coverage:

  • Semantic Scholar API key — Phase 2
    (citation verification) uses the public unauthenticated Semantic Scholar
    endpoint by default (≤1 QPS). A free API key raises the rate limit and
    reduces 429 back-off during large runs. The bundled
    scripts/s2_search.py reads SEMANTIC_SCHOLAR_API_KEY from the
    environment automatically — if the variable is absent it silently falls
    back to unauthenticated mode. The repo never commits a key.

    export SEMANTIC_SCHOLAR_API_KEY="your-key-here"   # https://api.semanticscholar.org/
    # verify it's picked up:
    python skills/literature-review-agent/scripts/s2_search.py --check-key
    

    See skills/literature-review-agent/references/s2-api-cookbook.md for
    endpoint details, field reference, and error-handling notes.

  • Exa — research-paper-focused search engine. The
    literature-review-agent can use it as a Phase 1 candidate-discovery
    backend via skills/literature-review-agent/scripts/exa_search.py. Set
    EXA_API_KEY in your environment (the repo never commits a key) and the
    helper queries Exa with category: "research paper", returning 10–20
    candidates per query in the format the rest of the pipeline expects. See
    skills/literature-review-agent/references/exa-search-cookbook.md for
    the full recipe, query patterns, cost (~$0.007/query), and security
    notes.

    export EXA_API_KEY="your-key-here"   # https://dashboard.exa.ai/
    python skills/literature-review-agent/scripts/exa_search.py \
        --query "Sparse attention long context" --num-results 15
    

    Skip Exa entirely if your host (Claude Code, Cursor, Antigravity) already
    has a native web search tool — the agent will use that instead.

Quickstart

# 1. scaffold a workspace next to your raw materials
python skills/paper-orchestra/scripts/init_workspace.py --out workspace/

# 2. drop your inputs into workspace/inputs/
#    (idea.md, experimental_log.md, template.tex, conference_guidelines.md;
#     optional pre-existing figures go in workspace/inputs/figures/)

# 3. ask your coding agent:
#    "Run the paper-orchestra pipeline on ./workspace"

A ready-to-run toy case lives at examples/minimal/.

Repo layout

paper-orchestra/
├── README.md, LICENSE, CITATION.cff, requirements.txt
├── skills/                  # 7 skills + orchestrator
├── examples/minimal/        # toy end-to-end example
└── docs/
    ├── architecture.md      # deep-dive on the pipeline
    ├── paper-fidelity.md    # design-decision → paper page map
    └── coding-agent-integration.md  # per-host setup

Fidelity to the paper

Every agent prompt in skills/*/references/prompt.md is reproduced verbatim from Appendix F of arXiv:2604.05018, with a header pointing to the page number. See docs/paper-fidelity.md for a design-decision → paper-page map.

On top of the paper, this repo adds a few deterministic hardening scripts (orphan-citation gate, anti-leakage grep, worklog-based rollback, provenance snapshots). These are clearly marked as out-of-paper improvements in docs/paper-fidelity.md.

Citation

If you use this skill pack, please cite the paper:

@article{song2026paperorchestra,
  title={{PaperOrchestra}: A Multi-Agent Framework for Automated {AI} Research Paper Writing},
  author={Song, Yiwen and Song, Yale and Pfister, Tomas and Yoon, Jinsung},
  journal={arXiv preprint arXiv:2604.05018},
  year={2026},
  url={https://arxiv.org/pdf/2604.05018}
}
It would have been fun if the repo wrote the paper.

License

MIT — see LICENSE.

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