The Re-Align Hackathon

Challenge Overview • Getting Started • Submission Process

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Welcome to the Re-Align Workshop Hackathon! This challenge focuses on understanding and comparing representational alignment across a wide variety of vision models. Join either the 🟦 Blue Team or 🟥 Red Team and compete to discover whether representations are universal or idiosyncratic.

📚 Research Context Representational alignment has emerged as both an implicit and explicit goal in many machine learning subfields, including knowledge distillation (Hinton et al., 2015), disentanglement (Montero et al., 2022), and concept-based models (Koh et al., 2020). The concept has been explored under various terms including latent space alignment, conceptual alignment, and representational similarity analysis (Kriegeskorte et al., 2008; Peterson et al., 2018; Roads & Love, 2020; Muttenthaler et al., 2023). Recent work has leveraged human perceptual judgments to enrich representations within vision models (Sundaram et al., 2024), while other research explores using brain signals to fine-tune semantic representations in language models. However, there remains little consensus on which metrics best identify similarity between systems (Harvey et al., 2024; Schaeffer et al., 2024). Representational alignment can help machines learn useful representations from humans with less supervision (Fel et al., 2022; Muttenthaler et al., 2023; Sucholutsky & Griffiths, 2023), while also uncovering opportunities for humans to leverage domain-specific representations from machines when designing hybrid systems (Steyvers et al., 2022; Shin et al., 2023; Schut et al., 2023).

🏁 Quickstart

1. Choose your team: 🟦 Blue (universality) or 🟥 Red (idiosyncracy).
2. Fork and/or the repository following our setup instructions.
3. Explore the example notebooks in our starter code section.
4. Submit your findings using our submission process.

Good luck! 🍀

🎯 Challenge Overview

We have over 1000 vision models available to adjudicate This hackathon seeks to answer fundamental questions that have driven recent research in representational alignment (Sucholutsky et al., 2023; Muttenthaler et al., 2024). Participants will join either a 🟦 Blue Team or a 🟥 Red Team, and provide JSON submissions that demonstrate the largest uniform set of models (🟦) or greatest differentiation among those models (🟥).

🟦 Blue Team

"Find similarities." Building on work showing that different networks can learn similar representations at scale, Blue Teams search for cases where this convergence occurs.

• 🎯 Objective: Submit a collection of models demonstrating representational and/or functional equivalence.
• 💡 Challenge: Discover similarities among different architectures.
• 🏅 Victory condition: Largest uniform set of representationally and/or functionally identical models.

🟥 Red Team

"Drive distinctions." Following approaches that examine models presumed to be aligned to uncover representational differences, Red Teams develop stimuli that drive misalignment in model representations.

• 🎯 Objective: Curate stimuli that reveal representational and/or functional differences.
• 💡 Challenge: Identify the most informative test cases highlighting variation.
• 🏅 Victory condition: Greatest differentiation among model representations and/or behaviors.

🚀 Getting Started

1️⃣ Fork and/or Clone Repository

Optional: Do you want to make a submission? Then fork this repository:

Click the Fork button near the top of the page to fork your own copy of representational-alignment/hackathon. You must be logged into GitHub.

⚠️ Important: Uncheck "Copy the main branch only" to include all branches, including branches used for submission!

Clone and setup:

git clone [YOUR_FORK_URL]
cd hackathon/
git checkout main
git checkout -b <team_color>_team_submissions  # blue or red

2️⃣ Environment Setup

Install uv and dependencies:

# Install uv if you haven't already.
# On macOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Setup environment
uv sync

# Activate environment for Python commands
source .venv/bin/activate

3️⃣ Example Notebooks

We've provided starter notebooks to help you get started with the hackathon. These can be found in examples/.

Use this command to launch them!

# Example: Launch Jupyter Lab
uv run --with jupyter jupyter lab

Notebook	Purpose	Teams
📊 extract_activations.ipynb	Extract model activations	🟦🟥 Both
🟦 blue_team_starter.ipynb	Identify model similarities	🟦 Blue
🟥 red_team_starter.ipynb	Find differentiating stimuli	🟥 Red

4️⃣ Submission

See below!

📤 Submission Process

📋 Submission Requirements

Your final submission must be in JSON format and submitted as a pull request (PR), and should include a brief textual explanation of your findings. See examples of a Blue Team and a Red Team submission.

🟦 Blue Team JSON Format

{
    "models": [
        {
            "model_name": "model1_name",
            "source": "where the model is from",
            "model_parameters": {
                "param1": "value1",
                "param2": "value2"
            }
        },
        {
            "model_name": "model2_name",
            "source": "where the model is from",
            "model_parameters": null
        }
    ]
}

🟥 Red Team JSON Format

{
    "differentiating_images": [
        {
            "dataset_name": "cifar100",
            "image_identifier": "test/girl/image_987.png"
        },
        {
            "dataset_name": "cifar100",
            "image_identifier": "test/orange/image_19.png"
        },
        {
            "dataset_name": "cifar100",
            "image_identifier": "test/bottle/image_2428.png"
        }
    ]
}

📝 Field Descriptions

Key	Purpose	Example
dataset_name	Name of the public dataset	"cifar100"
image_identifier	Path/ID within your stimuli/ directory	"test/girl/image_987.png"

⚠️ Important: Include exactly these two keys for every stimulus. Ensure every (dataset_name, image_identifier) pair is unique.

📁 File Organization

Team	Directory	Filename	Commit Title
🟦 Blue	blue_team_submissions/	team_name.json	Blue Team Submission: [team_name]
🟥 Red	red_team_submissions/	team_name.json	Red Team Submission: [team_name]

🔄 Submit Your Work

# Add your files
git add [your_files]

# Commit with proper message
git commit -m "<Blue or Red> Team Submission: [team_name]"

# Push to your fork
git push --set-upstream origin <team_color>_team_submissions

🎯 Create Pull Request

1. Go to your fork on GitHub
2. Click "Compare & pull request"
3. Set base branch: <team_color>_team_submissions
4. Title: Blue Team: [team_name] or Red Team: [team_name]
5. Submit! 🎉