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README.md
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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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Paper: [Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents](https://arxiv.org/abs/2412.00821) [AAAI 2026 TrustAgent]
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