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  PhyscisQA dataset comprises 370 carefully selected high school physics questions sourced from online resources. These
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  questions are notably complex, often requiring the application of multiple concepts, intricate computations, and multihop reasoning. Each question is paired
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  with a comprehensive, step-by-step solution, to support the evaluation of LLMs for physics reasoning.PhysicsQA offers a more robust evaluation
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- and analysis of LLM performance by encompassing a diverse range of questions, both in terms of complexity and the topics covered.
 
 
 
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  PhyscisQA dataset comprises 370 carefully selected high school physics questions sourced from online resources. These
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  questions are notably complex, often requiring the application of multiple concepts, intricate computations, and multihop reasoning. Each question is paired
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  with a comprehensive, step-by-step solution, to support the evaluation of LLMs for physics reasoning.PhysicsQA offers a more robust evaluation
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+ and analysis of LLM performance by encompassing a diverse range of questions, both in terms of complexity and the topics covered.
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+
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+ Paper: [Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents](https://arxiv.org/abs/2412.00821) [AAAI 2026 TrustAgent]