BoxingGym, proposed in this paper,  lets AI scientists test hypotheses.

BoxingGym introduces a benchmark framework to test how well LLMs can design experiments and discover scientific models through interactive experimentation and natural language communication.

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🧪 Techniques in this Paper:

→ BoxingGym provides 10 environments implemented as generative probabilistic models from various scientific domains like psychology and ecology

→ Each environment allows agents to run interactive experiments and collect data through a flexible language-based interface

→ The framework evaluates experimental design using Expected Information Gain (EIG) to measure how much experiments reduce model uncertainty

→ For model discovery evaluation, agents must explain their findings to a novice agent who then makes predictions

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💡 Key Insights:

→ Scientific discovery requires tight coupling between experimental design and model building

→ Language-based interfaces enable flexible scientific theory representation and communication

→ Evaluation through explanation tests both model accuracy and interpretability

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📊 Results:

→ Current LLMs (GPT-4o) struggle with both experimental design and model discovery tasks

→ Adding statistical modeling (Box's Apprentice) doesn't consistently improve performance

→ LLMs show better results when they have accurate prior domain knowledge