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PHYRE: A New Benchmark for Physical Reasoning
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Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit https://player.phyre.ai.
Forward citations
Cited by 3 Pith papers
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Object-centric Denoising Diffusion Models for Physical Reasoning
An object-centric diffusion model generates multi-object trajectories with conditioning at arbitrary time steps, demonstrated on the PHYRE physics benchmark.
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ABench-Physics: Benchmarking Physical Reasoning in LLMs via High-Difficulty and Dynamic Physics Problems
A new physics benchmark with static and dynamically varied numeric problems shows top LLMs solve at most 43 percent of the static set and drop sharply when problem constants change.
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CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
CausalVQA provides 793 paired real-video causal reasoning questions on which the best multimodal model scores 61.66% versus 84.78% for humans, with the largest gaps on anticipation and hypothetical questions.
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