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NEWTON: Are Language Models Capable of Physical Reasoning?
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2026-03-08 17:18:21
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NEWTON Code Dataset NEWTON is a Repository , Pipeline , and Benchmark designed to evaluate the physical reasoning capability of LLMs Abstract Training language models on extensive unprocessed text data has yielded impressive advancements in natural language processing (NLP), particularly in tasks such as question answering and reading comprehension. These models, through their contextualized representations, have been proven in numerous studies to encapsulate syntactic, semantic, word sense, and common-sense knowledge. However, there has been limited exploration of their physical reasoning abilities, specifically concerning the crucial attributes for comprehending everyday objects. To address this gap, we introduce NEWTON dataset, a comprehensive Repository, Pipeline, and Benchmark designed to facilitate streamlined evaluation of Large Language Models (LLMs) in the context of physical reasoning. The dataset Repository comprises a vast collection of object-att...
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