paper-review

Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.z...

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 [e-mail]

# 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 [e-mail]

# 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 [e-mail]

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):

• 1–2: Reject (major flaws or well-known results)
• 3: Borderline reject
• 4: Borderline accept
• 5–6: Accept / Strong accept

Correctness score (1–3):

• 1: No objective errors
• 2: Minor errors (notation, arithmetic in non-critical parts)
• 3: Major errors (wrong proofs, core algorithm flaws)

BibTeX verification:

• verified: found in CrossRef/arXiv/DBLP
• suspect: title/author mismatch or not found — manual check recommended

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

• Full PipelineConfig options: reference.md
• Discipline details and venues: reference.md

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

• The API key is wrong or not set.
• Check the correct env var for the provider (see Model selection table).
• For DashScope: echo $DASHSCOPE_API_KEY — must be non-empty.
• Fix: export the correct key and re-run.

NotFoundError / 404 — model not found

• The model name string is wrong.
• Search the web for the provider's current model list and use the exact API ID.
• Common mistakes: using a ChatGPT UI name instead of the API ID, outdated snapshot suffix.
• Fix: correct --model and re-run.

BadRequestError / 400

• Often caused by --base_url ending with /v1/chat/completions instead of /v1. litellm appends the path automatically — strip everything after /v1.
• May also indicate the model does not support vision/image input. Use a vision-capable model (see Model selection) or omit --vision.
• Fix: correct --base_url or switch to a vision-capable model and re-run.

Connection error / endpoint not reachable

• --base_url points to the wrong host or port.
• Test the endpoint first: curl <base_url>/models -H "Authorization: Bearer <key>"
• Fix: correct --base_url to the reachable endpoint and re-run.

Timeout

• The model is taking too long (common for long PDFs with vision mode).
• Fix: increase --timeout (default 7500 s) or reduce --vision_max_pages.

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

Never summarise or interpret the paper yourself as a substitute for a failed pipeline run.