MaD Physics: Evaluating information seeking under constraints in physical environments
Researchers have introduced MaD Physics, a new benchmark designed to evaluate the capabilities of artificial intelligence agents in scientific discovery processes constrained by resource limitations. Unlike existing benchmarks that focus on static reasoning or unconstrained tasks, MaD Physics assesses an agent's ability to make informative measurements and plan effectively under strict quality and quantity budgets. The framework comprises three distinct environments based on altered physical laws to prevent knowledge contamination, requiring agents to infer underlying models and predict future system states after exhausting their measurement budget. This approach targets two core scientific competencies: model inference from data and constrained planning. The study also demonstrates the benchmark's utility for evaluating multimodality and in-context learning. Initial tests using four Gemini models (2.5 Flash Lite, 2.5 Flash, 2.5 Pro, and 3 Flash) revealed significant shortcomings in their structured exploration and data collection strategies. These findings highlight critical areas for improvement in AI-driven scientific reasoning, offering a robust tool for developing more efficient and capable autonomous scientific agents.
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MaD Physics: Evaluating information seeking under constraints in physical environments
Researchers have introduced MaD Physics, a new benchmark designed to evaluate the capabilities of artificial intelligence agents in scientific discovery processes constrained by resource limitations. Unlike existing benchmarks that focus on static reasoning or unconstrained tasks, MaD Physics assesses an agent's ability to make informative measurements and plan effectively under strict quality and quantity budgets. The framework comprises three distinct environments based on altered physical laws to prevent knowledge contamination, requiring agents to infer underlying models and predict future system states after exhausting their measurement budget. This approach targets two core scientific competencies: model inference from data and constrained planning. The study also demonstrates the benchmark's utility for evaluating multimodality and in-context learning. Initial tests using four Gemini models (2.5 Flash Lite, 2.5 Flash, 2.5 Pro, and 3 Flash) revealed significant shortcomings in their structured exploration and data collection strategies. These findings highlight critical areas for improvement in AI-driven scientific reasoning, offering a robust tool for developing more efficient and capable autonomous scientific agents.
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