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LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . To solve this complex physical reasoning task, we present LLMPhy, a zero-shot black-box optimization framework that leverages the physics knowledge and program synthesis abilities of LLMs, and synergizes these abilities with the world models built into modern physics engines
LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . To solve this complex physical reasoning task, we present LLMPhy, a zero-shot black-box optimization framework that leverages the physics knowledge and program synthesis abilities of LLMs, and synergizes these abilities with the world models built into modern physics engines
A arXiv:2411. 08027v2 [cs. LG] 12 Dec 2024 ABSTRACT Physical reasoning is an important skill needed for robotic agents when operat-ing in the real world However, solving such reasoning problems often involves hypothesizing and reflecting over complex multi-body interactions under the ef-fect of a multitude of physical forces and thus learning all such interactions poses a significant hurdle for state-of-the-art machine learning
LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . Physical reasoning is an important skill needed for robotic agents when operating in the real world However, solving such reasoning problems often involves hypothesizing and reflecting over complex multi-body interactions under the effect of a multitude of physical forces and thus learning all such interactions poses a significant hurdle for state-of-the-art machine learning frameworks
LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . However, solving such reasoning problems often involves hypothesizing and reflecting over complex multi-body interactions under the effect of a multitude of physical forces and thus learning all such interactions poses a significant hurdle for state-of-the-art machine learning frameworks, including large language models (LLMs)
LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . LLMPhy is presented, a zero-shot black-box optimization framework that leverages the physics knowledge and program synthesis abilities of LLMs, and synergizes these abilities with the world models built into modern physics engines Physical reasoning is an important skill needed for robotic agents when operating in the real world However, solving such reasoning problems often involves
NEWTON: Are Large Language Models Capable of Physical Reasoning? The NEWTON benchmark consists of 160K QA questions, curated using the NEWTON repository to investigate the physical reasoning capabilities of several mainstream language models across foundational, explicit, and implicit reasoning tasks Through extensive empirical analysis, our results highlight the capabilities of LLMs for physical reasoning
LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . To solve this complex physical reasoning task, we present LLMPhyLLMPhy\operatorname{LLMPhy}roman_LLMPhy, a zero-shot black-box optimization framework that leverages the physics knowledge and program synthesis abilities of LLMs, and synergizes these abilities with the world models built into modern physics engines
LLMPhy: Complex Physical Reasoning Using Large Language Models and . . . The paper presents LLMPhy, a novel framework that integrates large language models with physics engines to enhance complex physical reasoning and object dynamics prediction in robotic applications, utilizing the TraySim dataset for effective zero-shot optimization