Condensed Matter · Physics & Astronomy
Disordered Systems & Neural Networks
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135 papers, from 4 September 2026 to 5 November 2026: 135 arXiv preprints.
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Newest papers, page 7
Random-matrix and transport frequencies in eigenstate spectral functions
The fact that generic isolated many-body quantum systems thermalize is understood using the eigenstate thermalization hypothesis (ETH).TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is guided by physical…Flag Game: A Toy Model for Mechanistic Swarm Interpretability
Emergent coordinated behaviors of AI agents are starting to present critical safety risks.Learning-Induced Dynamical Transition in Recurrent Neural Networks
Learning in recurrent neural networks can fundamentally reshape their underlying dynamics, transforming initially chaotic activity into stable task-dependent behavior.Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization
Neural networks provide expressive representations for scientific computing.A Data-free Universal Prior over Syntactic Structures
Probability is fundamental to theories of language comprehension, production, acquisition, and evolution, as well as to large language models.Bias-Induced Crossover in Absolute Capacity of Dense Associative Memory
The absolute capacity of dense associative memory has mainly been analyzed for unbiased patterns.Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data.Phases in a class of associative memories via hidden neurons
Associative memory in the Hopfield network is attractor dynamics in a disordered many-body system, and higher-order and exponential extensions turn its retrieval update into softmax attention.Learning structural balance of graphs from quantum spectral features
We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs.Hierarchical Prototype Emergence in Modern Hopfield Models
Hierarchical correlations are a universal feature of any realistic model of data, and the question of how associative memory models may learn these correlations and generalize beyond them to…Graph neural networks and the energetic cavity method for combinatorial optimization
We study the use of graph neural networks (GNNs) for finding approximate ground states of Ising models.Topology Obstructs Pure Foundation Neural Quantum States
Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase diagrams.Physical policy gradient theorem for in situ stochastic-adjoint training
In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems.Coarse-Graining Hidden Representations: Unsupervised Neuron Selection via Mapping Entropy
Overparameterized neural networks carry far more hidden units than a task nominally requires, raising the question of which neurons are essential and whether that distinction is legible in the…