Papers with physics reasoning

3 papers
NEWTON: Are Large Language Models Capable of Physical Reasoning? (2023.findings-emnlp)

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Challenge: Large Language Models have been shown to encapsulate syntactic, semantic, word sense, and common-sense knowledge, but limited exploration of their physical reasoning abilities has been conducted.
Approach: They propose a repository and benchmark to evaluate LLMs' physical reasoning skills . they use a pipeline to generate a variant of the benchmark customized to the objects and attributes relevant for their application.
Outcome: The proposed benchmark examines the reasoning capabilities of language models across reasoning tasks.
PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions (2025.findings-emnlp)

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Challenge: Current physics benchmarks focus on text-only inputs or only on problem-solving . current physics reasoning benchmarks neglect critical intermediate steps of variable identification and process formulation.
Approach: a new benchmark evaluates multimodal large language models in physics reasoning . the benchmark measures variables, process formulations, and solution derivation .
Outcome: PhysicsArena is the first multimodal physics reasoning benchmark . it evaluates MLLMs across three critical dimensions: variable identification, process formulation, and solution derivation.
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)

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Challenge: a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning.
Approach: They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics.
Outcome: The proposed model can be used to solve Olympiad-level physics problems.

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