📚 [Paper] | 📝 [Blog Post] | 📂 [Drive Folder]

One of the grand challenges of artificial intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used to aid human scientists, e.g. for brainstorming ideas or writing code, they still require extensive manual supervision or are heavily constrained to a specific task.

We're excited to introduce The AI Scientist, the first comprehensive system for fully automatic scientific discovery, enabling Foundation Models such as Large Language Models (LLMs) to perform research independently.

We further provide all runs and data from our paper here, where we run each base model on each template for ~50 ideas. We highly recommend reading through some of the Claude papers, (especially the diffusion ones), to get a sense of its strengths and weaknesses. Here are some example papers generated by The AI Scientist 📝:

1. DualScale Diffusion: Adaptive Feature Balancing for Low-Dimensional Generative Models
2. Multi-scale Grid Noise Adaptation: Enhancing Diffusion Models For Low-dimensional Data
3. GAN-Enhanced Diffusion: Boosting Sample Quality and Diversity
4. DualDiff: Enhancing Mode Capture in Low-dimensional Diffusion Models via Dual-expert Denoising
5. StyleFusion: Adaptive Multi-style Generation in Character-Level Language Models
6. Adaptive Learning Rates for Transformers via Q-Learning
7. Unlocking Grokking: A Comparative Study of Weight Initialization Strategies in Transformer Models
8. Grokking Accelerated: Layer-wise Learning Rates for Transformer Generalization
9. Grokking Through Compression: Unveiling Sudden Generalization via Minimal Description Length
10. Accelerating Mathematical Insight: Boosting Grokking Through Strategic Data Augmentation

Note: Caution! This codebase will execute LLM-written code. There are various risks and challenges associated with this autonomy. This includes e.g. the use of potentially dangerous packages, web access, and potential spawning of processes. Use at your own discretion. Please make sure to containerize and restrict web access appropriately.

1. Requirements
2. Run AI Scientist Paper Generation Experiments
3. Getting an LLM Generated Paper Review
4. Making your own Template
5. Template Resources
6. Citing The AI Scientist
7. Frequently Asked Questions
8. Containerization

conda create -n ai_scientist python=3.11
conda activate ai_scientist
# Install pdflatex
sudo apt-get install texlive-full

# Install pypi requirements
pip install -r requirements.txt

When installing texlive-full, you may need to hold Enter.

By default, this uses the OPENAI_API_KEY environment variable.

By default, this uses the ANTHROPIC_API_KEY environment variable.

For Claude models provided by Amazon Bedrock, please install these additional packages:

pip install anthropic[bedrock]

Next, specify a set of valid AWS Credentials and the target AWS Region:

Set these environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION_NAME.

For Claude models provided by Vertex AI Model Garden, please install these additional packages:

pip install google-cloud-aiplatform
pip install anthropic[vertex]

Next, set up a valid authentication for a Google Cloud project, for example by providing region and project ID like so:

export CLOUD_ML_REGION="REGION" # for Model Garden call
export ANTHROPIC_VERTEX_PROJECT_ID="PROJECT_ID" # for Model Garden call
export VERTEXAI_LOCATION="REGION" # for Aider/LiteLLM call, as per https://docs.litellm.ai/docs/providers/vertex#set-vertex-project--vertex-location
export VERTEXAI_PROJECT="PROJECT_ID" # for Aider/LiteLLM call as per https://docs.litellm.ai/docs/providers/vertex#set-vertex-project--vertex-location

By default, this uses the DEEPSEEK_API_KEY environment variable.

By default, this uses the OPENROUTER_API_KEY environment variable.

Our code can also optionally use a Semantic Scholar API Key (S2_API_KEY) for higher throughput if you have one, though in principle it should work without it.

Be sure to provide the key for the model used for your runs, e.g.

export OPENAI_API_KEY="YOUR KEY HERE"
export S2_API_KEY="YOUR KEY HERE"

# Prepare NanoGPT data
python data/enwik8/prepare.py
python data/shakespeare_char/prepare.py
python data/text8/prepare.py

# Set up NanoGPT baseline run
# NOTE: YOU MUST FIRST RUN THE PREPARE SCRIPTS ABOVE!
cd templates/nanoGPT && python experiment.py --out_dir run_0 && python plot.py

# NOTE: YOU MUST FIRST RUN THE PREPARE SCRIPTS ABOVE!
cd templates/nanoGPT_lite && python experiment.py --out_dir run_0 && python plot.py

# Set up 2D Diffusion
git clone https://github.com/gregversteeg/NPEET.git
cd NPEET
pip install .
pip install scikit-learn

# Set up 2D Diffusion baseline run
cd templates/2d_diffusion && python experiment.py --out_dir run_0 && python plot.py

# Set up Grokking
pip install einops

# Set up Grokking baseline run
cd templates/grokking && python experiment.py --out_dir run_0 && python plot.py

Note: please ensure the setup steps above are completed.

conda activate ai_scientist
# Run the paper generation.
python launch_scientist.py --model "gpt-4o-2024-05-13" --experiment nanoGPT_lite --num-ideas 2
python launch_scientist.py --model "claude-3-5-sonnet-20240620" --experiment nanoGPT_lite --num-ideas 2

import openai
from ai_scientist.perform_review import load_paper, perform_review

client = openai.OpenAI()
model = "gpt-4o-2024-05-13"

# Load paper from pdf file (raw text)
paper_txt = load_paper("report.pdf")
# Get the review dict of the review
review = perform_review(
    paper_txt,
    model,
    client,
    num_reflections=5,
    num_fs_examples=1,
    num_reviews_ensemble=5,
    temperature=0.1,
)

# Inspect review results
review["Overall"]  # overall score 1-10
review["Decision"]  # ['Accept', 'Reject']
review["Weaknesses"]  # List of weaknesses (str)

To run batch analysis:

cd review_iclr_bench
python iclr_analysis.py --num_reviews 500  --batch_size 100 --num_fs_examples 1 --num_reflections 5 --temperature 0.1 --num_reviews_ensemble 5

If there is an area of study you would like The AI Scientist to explore, it should be very easy to create your own templates. In general, follow the structure of the existing templates, which consists of:

• experiment.py -- This is a single file where the 'meat' of the content is. It takes in an argument for out_dir, which is where it should create the folder and save the relevant information from the run.
• plot.py -- This should take in the information from the run folders and create plots. The code should be clear and easy to edit.
• prompt.json -- Put information about your template here.
• seed_ideas.json -- Put example ideas here. You can also try to generate ideas without any examples, and then pick the best one or two to put here.
• latex/template.tex -- We recommend using our latex folder, but be sure to replace the pre-loaded citations with ones that you would expect to be more relevant.

We provide 3 templates, which heavily use code from other repositories, which we credit below. (Normally, we would do this in the files themselves, but it's unclear how this would affect The AI Scientist since it would be visible).

The NanoGPT template used code from NanoGPT and this PR.

The 2D Diffusion template used code from tiny-diffusion, ema-pytorch, and Datasaur.

The Grokking template used code from Sea-Snell/grokking and danielmamay/grokking.

We would like to thank the developers of the open-source models and packages for their contributions and for making their work available.

If you use The AI Scientist in your research, please cite it as follows:

@article{lu2024aiscientist,
  title={The {AI} {S}cientist: Towards Fully Automated Open-Ended Scientific Discovery},
  author={Lu, Chris and Lu, Cong and Lange, Robert Tjarko and Foerster, Jakob and Clune, Jeff and Ha, David},
  journal={arXiv preprint arXiv:2408.06292},
  year={2024}
}

We recommend reading our paper in the first instance for any questions you have on The AI Scientist.

Make sure you have completed all the setup and preparation steps before the main experiment script.

The AI Scientist finishes an idea with a success rate that depends on both the template, the base foundation model, and the complexity of the idea. We advise referring to our main paper. The highest success rates are observed with Claude Sonnet 3.5. Reviews are best done with GPT-4o, all other models have issues with positivity bias or failure to conform to required outputs.

Typically less than $15 per paper with Claude Sonnet 3.5. We recommend DeepSeek Coder V2 for a much more cost-effective approach. A good place to look for new models is the Aider leaderboard.

Change the base template.tex files contained within each template.

Please refer to the instructions for different templates. In this current iteration, this is restricted to ideas that can be expressed in code. However, lifting this restriction would represent exciting future work! :)

Please see this PR for an example of how to add a new model, e.g. this time for Claude via Bedrock. We do not advise any model that is significantly weaker than GPT-4 level for The AI Scientist.

We include a community-contributed Docker image that may assist with your containerization efforts in experimental/Dockerfile.

You can use this image like this:

# Endpoint Script
docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -v `pwd`/templates:/app/AI-Scientist/templates <AI_SCIENTIST_IMAGE> \
  --model gpt-4o-2024-05-13 \
  --experiment 2d_diffusion \
  --num-ideas 2

# Interactive
docker run -it -e OPENAI_API_KEY=$OPENAI_API_KEY \
  --entrypoint /bin/bash \
  <AI_SCIENTIST_IMAGE>