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AI-powered academic paper reviewer

Project description

OpenAIReview

AI-powered academic paper reviewer that detects technical and logical errors using LLMs.

Installation

uv pip install openaireview

For development:

uv pip install -e .

PDF math support (optional)

For math-heavy PDFs, install Marker separately to get accurate LaTeX extraction. Without Marker, PDFs are processed with PyMuPDF which cannot extract math symbols correctly.

# Install Marker CLI in an isolated environment (avoids dependency conflicts)
uv tool install marker-pdf

Marker is used automatically when available on PATH. For papers with math, we recommend using .tex source or arXiv HTML URLs instead of PDF when possible — these always produce correct output.

Quick Start

First, set your OpenRouter API key (get one at openrouter.ai/keys):

export OPENROUTER_API_KEY=your_key_here

Or create a .env file in your working directory:

OPENROUTER_API_KEY=your_key_here

Then review a paper and visualize results:

# Review a local file
openaireview review paper.pdf

# Or review directly from an arXiv URL
openaireview review https://arxiv.org/html/2310.06825

# Visualize results
openaireview serve
# Open http://localhost:8080

CLI Reference

openaireview review <file_or_url>

Review an academic paper for technical and logical issues. Accepts a local file path or an arXiv URL.

Option Default Description
--method incremental Review method: zero_shot, local, incremental, incremental_full
--model anthropic/claude-opus-4-6 Model to use
--output-dir ./review_results Directory for output JSON files
--name (from filename) Paper slug name

openaireview serve

Start a local visualization server to browse review results.

Option Default Description
--results-dir ./review_results Directory containing result JSON files
--port 8080 Server port

Supported Input Formats

  • PDF (.pdf) — uses Marker for high-quality extraction with LaTeX math; falls back to PyMuPDF if Marker is not installed
  • DOCX (.docx) — via python-docx
  • LaTeX (.tex) — plain text with title extraction from \title{}
  • Text/Markdown (.txt, .md) — plain text
  • arXiv HTML — fetch and parse directly from https://arxiv.org/html/<id> or https://arxiv.org/abs/<id>

Environment Variables

Variable Default Description
OPENROUTER_API_KEY (required) Your OpenRouter API key
MODEL anthropic/claude-opus-4-6 Default model

These can be set as environment variables or in a .env file. See .env.example for a template.

Supported Models & Pricing

All models available on OpenRouter are supported — use any model ID via --model. The following models have built-in pricing for accurate cost tracking in the visualization:

Model Input ($/1M tokens) Output ($/1M tokens)
anthropic/claude-opus-4-6 $5.00 $25.00
anthropic/claude-opus-4-5 $5.00 $25.00
openai/gpt-5.2-pro $21.00 $168.00
google/gemini-3.1-pro-preview $2.00 $12.00

For models not listed above, a default rate of $5.00/$25.00 per 1M tokens is used.

Review Methods

  • zero_shot — single prompt asking the model to find all issues
  • local — deep-checks each chunk with surrounding window context (no filtering)
  • incremental — sequential processing with running summary, then consolidation
  • incremental_full — same as incremental but returns all comments before consolidation

Benchmarks

Benchmark data and experiment scripts are in benchmarks/. See benchmarks/REPORT.md for results.

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