paper-review

Paper Review Skill

Multi-stage academic paper review using the OpenJudge PaperReviewPipeline:

1. Safety check — jailbreak detection + format validation 2. Correctness — objective errors (math, logic, data inconsistencies) 3. Review — quality, novelty, significance (score 1–6) 4. Criticality — severity of correctness issues 5. BibTeX verification — cross-checks references against CrossRef/arXiv/DBLP

Prerequisites

# Install OpenJudge
pip install py-openjudge
Extra dependency for paper_review
pip install litellm
pip install pypdfium2  # only if using vision mode (use_vision_for_pdf=True)

Gather from user before running

| Info | Required? | Notes | |------|-----------|-------| | Paper file path | Yes | PDF or .tar.gz/.zip TeX package | | API key | Yes | Env var preferred: OPENAI_API_KEY, ANTHROPIC_API_KEY, etc. | | Model name | No | gpt-5.2, anthropic/claude-opus-4-6, dashscope/qwen-vl-plus. See Model selection below | | Discipline | No | If not given, uses general CS/ML-oriented prompts | | Venue | No | e.g. "NeurIPS 2025", "The Lancet" | | Instructions | No | Free-form reviewer guidance, e.g. "Focus on experimental design" | | Language | No | "en" (default) or "zh" for Simplified Chinese output | | BibTeX file | No | Required only for reference verification | | CrossRef email | No | Improves API rate limits for BibTeX verification |

Quick start

File type is auto-detected: .pdf → PDF review, .tar.gz/.zip → TeX review, .bib → BibTeX verification.

# Basic PDF review
python -m cookbooks.paper_review paper.pdf
With discipline and venue
python -m cookbooks.paper_review paper.pdf \
  --discipline cs --venue "NeurIPS 2025"
Chinese output
python -m cookbooks.paper_review paper.pdf --language zh
Custom reviewer instructions
python -m cookbooks.paper_review paper.pdf \
  --instructions "Focus on experimental design and reproducibility"
PDF + BibTeX verification
python -m cookbooks.paper_review paper.pdf \
  --bib references.bib --email your@email.com
Vision mode (for models that prefer images over text extraction)
python -m cookbooks.paper_review paper.pdf \
  --vision --vision_max_pages 30 --format_vision_max_pages 10
TeX source package
python -m cookbooks.paper_review paper_source.tar.gz \
  --discipline biology --email your@email.com
TeX source package with Chinese output and custom instructions
python -m cookbooks.paper_review paper_source.tar.gz \
  --language zh --instructions "This is a short paper, be concise"
Verify a standalone BibTeX file
python -m cookbooks.paper_review --bib_only references.bib --email your@email.com

All options

| Flag | Default | Description | |------|---------|-------------| | input (positional) | — | Path to PDF, TeX package, or .bib file | | --bib_only | — | Path to .bib file for standalone verification (no review) | | --model | gpt-4o | Model name | | --api_key | env var | API key | | --base_url | — | Custom API endpoint — must end at /v1, not /v1/chat/completions (litellm appends the path automatically) | | --discipline | — | Academic discipline | | --venue | — | Target conference/journal | | --instructions | — | Free-form reviewer guidance | | --language | en | Output language: en or zh | | --bib | — | Path to .bib file (for PDF review + reference verification) | | --email | — | CrossRef mailto for BibTeX check | | --paper_name | filename stem | Paper title in report | | --output | auto | Output .md report path | | --no_safety | off | Skip safety checks | | --no_correctness | off | Skip correctness check | | --no_criticality | off | Skip criticality verification | | --no_bib | off | Skip BibTeX verification | | --vision | on | Use vision mode (requires pypdfium2); enabled by default | | --vision_max_pages | 30 | Max pages in vision mode (0 = all) | | --format_vision_max_pages | 10 | Max pages for format check (0 = use --vision_max_pages) | | --timeout | 7500 | API timeout in seconds |

Interpreting results

Review score (1–6):

Correctness score (1–3):

BibTeX verification:

Model selection

This pipeline uses litellm for model calls. Provider prefixes are handled automatically by the pipeline — see the table below.

IMPORTANT: The model MUST support multimodal (vision) input. PDF review uses vision mode (--vision) to render pages as images, which requires a vision-capable model. Text-only models will fail or produce empty reviews.

The --model value uses a provider/model-name convention so the pipeline knows which API endpoint to call. The table below shows the exact string to pass:

| Provider | --model value | Env var | Notes | |----------|----------------|---------|-------| | OpenAI | gpt-5.2, gpt-5-mini, … | OPENAI_API_KEY | No prefix needed; gpt-5.2 is the current flagship vision model; check OpenAI models for the latest | | Anthropic | anthropic/claude-opus-4-6, anthropic/claude-sonnet-4-6, … | ANTHROPIC_API_KEY | Use anthropic/ prefix; claude-opus-4-6 is the current flagship; check Anthropic models for the latest | | DashScope (Qwen) | dashscope/qwen-vl-plus, dashscope/qwen-vl-max, … | DASHSCOPE_API_KEY | Use dashscope/ prefix; the pipeline auto-routes to DashScope’s OpenAI-compatible endpoint | | Custom endpoint | bare model name | --api_key + --base_url | Use the model name your endpoint expects; no prefix needed when --base_url is set |

> Note on prefixes: The dashscope/ and anthropic/ prefixes are interpreted by > the pipeline itself — do not add them to the actual API key or base URL. > For OpenAI models the bare model name (e.g. gpt-5.2) is sufficient.

If the user does not specify a model, choose one based on available API keys: 1. DASHSCOPE_API_KEY set → use dashscope/qwen-vl-plus (vision-capable) 2. OPENAI_API_KEY set → search web for the latest vision-capable OpenAI model and use it (currently gpt-5.2) 3. ANTHROPIC_API_KEY set → search web for the latest vision-capable Anthropic model and use it with anthropic/ prefix (currently anthropic/claude-opus-4-6)

Vision mode is enabled by default for PDF review. Pages are rendered as images, which preserves formatting, figures, and tables. To disable, pass --no_vision (not recommended). The model must support multimodal (vision) input.

Additional resources

Troubleshooting API errors

**CRITICAL: When the pipeline fails with an API error, you MUST diagnose and fix the root cause. Do NOT fall back to reading the PDF as plain text yourself and calling the API manually — this bypasses the entire review pipeline and produces incorrect, incomplete results.**

Diagnose by reading the full error message, then follow the checklist below:

AuthenticationError / 401

NotFoundError / 404 — model not found

BadRequestError / 400

Connection error / endpoint not reachable

Timeout

After fixing, always re-run the full pipeline command.

File type is auto-detected: .pdf → PDF review, .tar.gz/.zip → TeX review, .bib → BibTeX verification.

# Basic PDF review
python -m cookbooks.paper_review paper.pdf
With discipline and venue
python -m cookbooks.paper_review paper.pdf \
  --discipline cs --venue "NeurIPS 2025"
Chinese output
python -m cookbooks.paper_review paper.pdf --language zh
Custom reviewer instructions
python -m cookbooks.paper_review paper.pdf \
  --instructions "Focus on experimental design and reproducibility"
PDF + BibTeX verification
python -m cookbooks.paper_review paper.pdf \
  --bib references.bib --email your@email.com
Vision mode (for models that prefer images over text extraction)
python -m cookbooks.paper_review paper.pdf \
  --vision --vision_max_pages 30 --format_vision_max_pages 10
TeX source package
python -m cookbooks.paper_review paper_source.tar.gz \
  --discipline biology --email your@email.com
TeX source package with Chinese output and custom instructions
python -m cookbooks.paper_review paper_source.tar.gz \
  --language zh --instructions "This is a short paper, be concise"
Verify a standalone BibTeX file
python -m cookbooks.paper_review --bib_only references.bib --email your@email.com

# Install OpenJudge
pip install py-openjudge
Extra dependency for paper_review
pip install litellm
pip install pypdfium2  # only if using vision mode (use_vision_for_pdf=True)