标题： PhySense：基于原理的物理推理大型语言模型基准测试 显示英文标题

标题： PhySense: Principle-Based Physics Reasoning Benchmarking for Large Language Models

摘要： 大型语言模型（LLMs）快速发展，越来越能够应对包括物理学在内的复杂科学问题。尽管取得了这些进展，当前的LLMs往往无法模拟人类专家基于原理的简洁推理方式，而是生成冗长且晦涩的解决方案。这种差异凸显了它们在应用核心物理原理以实现高效且可解释的问题解决方面的能力差距。 为了系统地研究这一局限性，我们引入了PhySense，这是一个新颖的基于原理的物理推理基准测试，旨在通过指导原则让专家轻松解决，但对于没有优先考虑原理的LLMs来说却出人意料地困难。 我们的评估涵盖了多种最先进的LLMs和提示类型，揭示了它们一致未能与专家式的推理路径保持一致，为开发具有高效、稳健且可解释的基于原理的科学推理AI系统提供了见解。 显示英文摘要

摘要： Large language models (LLMs) have rapidly advanced and are increasingly capable of tackling complex scientific problems, including those in physics. Despite this progress, current LLMs often fail to emulate the concise, principle-based reasoning characteristic of human experts, instead generating lengthy and opaque solutions. This discrepancy highlights a crucial gap in their ability to apply core physical principles for efficient and interpretable problem solving. To systematically investigate this limitation, we introduce PhySense, a novel principle-based physics reasoning benchmark designed to be easily solvable by experts using guiding principles, yet deceptively difficult for LLMs without principle-first reasoning. Our evaluation across multiple state-of-the-art LLMs and prompt types reveals a consistent failure to align with expert-like reasoning paths, providing insights for developing AI systems with efficient, robust and interpretable principle-based scientific reasoning.