An iterative AI reasoning process proposes new dynamical dark energy equations of state that are competitive with traditional forms on supernova, BAO, and Planck data.
Nature Reviews Physics4(12), 761–769 (2022) arXiv:2204.01467 [cs.CY]
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AHOIS is a Socratic multi-agent AI that autonomously discovers and validates a random-interference encoding strategy for multimode fiber optics, achieving 76.97% MNIST and 83.17% Fashion-MNIST accuracy with 16x16 measurements of effective rank 56.9.
Diffusion models recover known ENSO variability structure from synthetic LIM data when given enough samples, but require pre-training on CMIP6 plus fine-tuning to match observations with the ~700 samples available in ERSSTv5.
citing papers explorer
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Physics-guided discovery of dynamical dark-energy equations of state through iterative AI reasoning
An iterative AI reasoning process proposes new dynamical dark energy equations of state that are competitive with traditional forms on supernova, BAO, and Planck data.
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Socratic agents for autonomous scientific discovery in high-dimensional physical systems
AHOIS is a Socratic multi-agent AI that autonomously discovers and validates a random-interference encoding strategy for multimode fiber optics, achieving 76.97% MNIST and 83.17% Fashion-MNIST accuracy with 16x16 measurements of effective rank 56.9.
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Learning Climate Variability from Scarce Data with Diffusion Models: A Test Case for ENSO
Diffusion models recover known ENSO variability structure from synthetic LIM data when given enough samples, but require pre-training on CMIP6 plus fine-tuning to match observations with the ~700 samples available in ERSSTv5.