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ChaosBench-Logic v2 is a large-scale benchmark of 40,886 questions over 165 dynamical systems that evaluates LLMs' logical reasoning abilities, revealing near-random performance on regime transition reasoning and systematic failure modes even in frontier models.
This article discusses how a 1955 computer experiment at Los Alamos National Laboratory revolutionized the understanding of chaos, likely referring to the Fermi-Pasta-Ulam-Tsingou problem.
Introduces QuChaTeR, a hybrid architecture combining wavelet-based preprocessing, chaotic maps, and variational quantum circuits with recurrent structures for earthquake prediction, demonstrating faster convergence and superior accuracy over classical and quantum baselines.
Introduces NormWear-2, a world model that encodes multivariate physiological signals and clinical interventions into a shared latent space, using chaos-theoretic balancing to improve long-horizon forecasting across daily life, point-of-care, and clinical settings.
Introduces horizon-constrained Rashomon sets to characterize how model multiplicity evolves in chaotic systems. The framework proves exponential contraction of predictive equivalence and develops decision-aligned algorithms that improve decision quality by 18-34%.