BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery (2501.01540v1)

Abstract: Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research. Proposing theories, designing experiments to test them, and then revising them based on data are fundamental to scientific discovery. Despite the significant promise of LLM-based scientific agents, no benchmarks systematically test LLM's ability to propose scientific models, collect experimental data, and revise them in light of new data. We introduce BoxingGym, a benchmark with 10 environments for systematically evaluating both experimental design (e.g. collecting data to test a scientific theory) and model discovery (e.g. proposing and revising scientific theories). To enable tractable and quantitative evaluation, we implement each environment as a generative probabilistic model with which a scientific agent can run interactive experiments. These probabilistic models are drawn from various real-world scientific domains ranging from psychology to ecology. To quantitatively evaluate a scientific agent's ability to collect informative experimental data, we compute the expected information gain (EIG), an information-theoretic quantity which measures how much an experiment reduces uncertainty about the parameters of a generative model. A good scientific theory is a concise and predictive explanation. Therefore, to quantitatively evaluate model discovery, we ask a scientific agent to explain their model and then assess whether this explanation enables another scientific agent to make reliable predictions about this environment. In addition to this explanation-based evaluation, we compute standard model evaluation metrics such as prediction errors. We find that current LLMs, such as GPT-4o, struggle with both experimental design and model discovery. We find that augmenting the LLM-based agent with an explicit statistical model does not reliably improve these results.

• The paper introduces a benchmark suite, BoxingGym, that evaluates LLM performance in experimental design and hypothesis testing.
• It employs ten diverse environments with Bayesian optimal experimental design to measure experiment informativeness using Expected Information Gain.
• Experiments reveal mixed LLM capabilities, emphasizing the need for integrating robust statistical reasoning with language-based models.

An Evaluation Framework for AI in Automated Experimental Design and Model Discovery

The paper "BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery" presents a novel framework aimed at evaluating the capabilities of LLMs within the context of scientific inquiry. It introduces BoxingGym, a benchmark suite offering ten distinct environments designed to systematically assess the abilities of AI-driven agents in both experimental design and model discovery. This paper is particularly centered on tasks that emulate scientific processes such as proposing hypotheses, experimenting to gather data, and refining models, all conducted through a language-based interface.

Framework Design and Implementation

BoxingGym is grounded in real-world scientific paradigms across domains like psychology and ecology, offering a varied evaluation suite. Each environment within BoxingGym employs a generative probabilistic model. This design choice aligns with Bayesian optimal experimental design (BOED) literature, allowing the measurement of an experiment's informativeness through Expected Information Gain (EIG). This framework provides a controlled setting where an AI agent can engage in virtual experimentation without the need for real-world resources.

The suite acknowledges the iterative and goal-directed nature of scientific exploration by permitting agents to set experiments based on high-level objectives. These objectives guide agents to collect data that improve model predictability and conciseness—central metrics for evaluating scientific theories.

Methodology

BoxingGym evaluates model discovery through a novel communication-based metric. This involves having AI agents distill their findings into explanations, which are then used by novice agents to make predictions in the absence of direct interaction. This approach underscores the significance of interpretability and effective communication in scientific modeling, reflecting real-world scientific collaborations.

The paper also introduces baseline experiments using two types of agents: a standard LLM (GPT-4o) and an enhanced version, dubbed Box's Apprentice. The latter combines statistical modeling capabilities to substantiate an LLM’s analysis and prediction tasks. These configurations highlight the current capabilities and limitations of LLM-based agents in conducting effective experimental design and model discovery.

Evaluation and Insights

The results from initial evaluations using BoxingGym indicate a mixed performance landscape. In several environments, notably those requiring a deep integration of prior knowledge with incoming data, the LLM struggled to improve its predictions post-experimentation. Interestingly, the incorporation of explicit statistical model-building in Box’s Apprentice did not consistently outperform standard LLMs, suggesting room for optimization concerning the integration of statistical reasoning with language-based modeling.

The paper unearthed promising areas for enhancement in crafting AI agents able to formulate and iteratively test hypotheses—a cornerstone of scientific discovery. Notably, environments like IRT (Item Response Theory) offered rich testing grounds for agents to demonstrate latent pattern recognition and hypothesis refinement competencies, though results suggest further exploration is necessary to refine these agents.

Conclusion and Future Scope

BoxingGym provides a substantial contribution to the AI field by offering an extensible, language-based evaluation suite that simulates rigorous scientific inquiry. The results affirm the potential of LLMs as scientific aides, yet underscore significant hurdles, primarily in integrating domain-specific insights and statistical reasonings robustly.

Future work can explore the integration of more complex human-like interactions and real-world cost considerations into these AI systems' experimental decision-making processes. Moreover, expanding the diversity of domains within BoxingGym could further demonstrate its robustness and general applicability.

This benchmark not only paves the way for systematically assessing AI capabilities in scientific realms but also aligns with the broader vision of AI systems augmenting human cognition, ultimately contributing to faster, more efficient scientific discoveries.